Decision Support Systems for E-Purchasing using Case-Based Reasoning and Rating Based Collaborative Filtering

Decision Support Systems or E-Purchasing using Case-Based Reasoning and Rating Based Collaborative Filtering ABSTRACT The amount o trade conducted electronically has grown dramatically since the wide introduction o the Internet. E- marketing has shown a stupendous growth by including many unique beneits to marketing such as interactive response, low costs in distributing inormation and media to a global audience. Purchasing takes place within this environment which is essentially live and in which developments are taking place all the time. In this ever-changing inormation rich business environment, purchasing proessionals must be able to recognize these changes, identiy their impacts on supply matters and know how to respond to them in the interests o their organization. So Decision Support Systems is the need o the hour or assuring results at a aster rate that best matches the buyers preerences and give valid recommendations. Hence a Two-Dimensional approach is proposed: First, the Case-based reasoning approach is used, which is a novel paradigm that solves a new problem by remembering a previous similar situation and reusing the inormation and knowledge o that situation to bring out similar cases at a aster rate depending on the predetermined similarity criteria. Second, the Slope One Predictors or Online Rating- Based Collaborative Filtering and the Item-to-Item collaborative iltering is combined to produce accurate recommendations, on the basis o resources rating given by the users to help each other ind better content. A Database driven approach is ollowed which is both easy to implement and can support a ull range o application. Categories and Subject Descriptors H.4.2 [Inormation Systems Applications]: Types o Systems - Decision Support (Recommender System based on CBR and Collaborative iltering) General Terms Experimentation Keywords E-marketing, Decision Support System, Case-based reasoning (CBR), Similarity Measures, Item-to-Item collaborative iltering, Slope One Predictors or Online Rating-Based Collaborative Filtering. 1. INTRODUCTION The amount o trade conducted electronically has grown dramatically since the wide introduction o the Internet. A wide variety o commerce such as electronic unds transer, supply chain management, E-marketing, online transaction processing, and electronic data interchange (EDI) are conducted o which the E-marketing has shown a stupendous growth by including many unique beneits to marketing such as interactive response, low costs in distributing inormation and media to a global audience. Purchasing takes place within this environment which is essentially live and in which developments are taking place all the time. This ever-changing business environment puts all organizations under pressure to conorm or adapt, or suer the consequences. As the global economy becomes more integrated, purchasing proessionals must be able to recognize these changes, identiy their impacts on supply matters and know how to respond to them in the interests o their organization. So a decision support system is the need o the hour or assuring products that best matches the buyers preerences. This decision support system should be able to produce the results at a aster rate and at the same time it should give valid recommendations. Both the results and the recommendations should be on the basis o the users rating on the product and his own liking. Thus results shall be approached at a aster rate with more alternatives based on users preerences promising higher market eiciency. Some o the existing Decision Support Systems use Rule Induction algorithm which takes in a set o training examples beore the target problem is even known thus perorming eager generalization. But in complex domains such as E-purchasing which has multiple attributes, the algorithm ails as there are myriad ways to generalize a case. When the GA approach is used even though it is easy to set up a knowledge base, due to the dynamic nature o the environment, it is very diicult to expand the rule based system where adding a rule oten means re-writing a large part o the rules. Further when the algorithm terminates with a large number o generations, it is not assured that a satisactory solution is reached. When decision trees are used all decision actors must be changed into common units, which is not possible as the multi attribute inputs are both qualitative and quantitative in nature. In order to overcome the existing diiculties we propose a Two- Dimensional approach. In one hand we use the Case-based reasoning approach, which is one o popular methodologies in knowledge management [1] to bring out the results at a aster rate. The Case-based reasoning approach is used, which is a novel paradigm that solves a new problem by remembering a previous similar situation and reusing the inormation and knowledge o that situation to bring out similar cases at a aster rate depending on the predetermined similarity criteria [2]. On the other hand we combine Slope One Predictors or Online Rating-Based Collaborative Filtering and the Item-to-Item collaborative iltering to produce accurate recommendations, on the basis o resources rating given by the users to help each other to ind better content. A Database driven approach is ollowed

which is both easy to implement and can support a ull range o application. This paper is organized as ollows: In section 2, Decision Support System is discussed then in Section 3, Architecture o our system is proposed and Section 4, gives a picture o the Algorithm and Methodology that involves Case-based reasoning and Collaborative Filtering approach; Next, results and description is explained and illustrated as a part o implementation detail in section 5. Finally, in section 6, conclusions are provided and the uture extensions are suggested. 2. DECISION SUPPORT SYSTEM (DSS) A decision support system is a computer-based inormation system that supports people engaged in decision-making activities or semi structured problems. Here the term, support is intended to mean that the system assists human decision makers to exercise judgement (i.e.), the system is an aid or the person or persons making the decision. It also means that system does not make the decision (i.e.), the system helps decision makers exercise judgment but does not replace the human decision makers. A computerized decision support system may be needed or various reasons. Most o the managerial decisions are taken by groups o responsible managers. This is because, nowadays, due to the inormation revolution, any business system in the global scenario is solving complex decision problems requently. Some o the problems associated with this are diiculties in determining the most eective solution, varied nature o decision-makers domains, decision makers are generally not very comortable using intelligent systems, large number o mathematical, statistical calculations, selection procedures and methodologies. So the decision support systems provide speedy computations at low cost, which increases productivity o sta members and analysts. This provides high technical support or complex computations. Best quality support or choice o best among alternatives. It has a competitive edge to adapt to revolutions and overcoming cognitive limits in processing in storage. The DSS application can be depicted in Figure 1. The beneits o the decision support systems are that it acilitates the attainment o organizational objectives and access to inormation. DSS allows the user to be more productive. Increases the quantity o decision and provides competitive advantage. Saves time or user and increases communication capacity and quality o use. It provides better control in the organization. It allows the anticipation o problems and opportunities. It also allows planning and a search or the cause o a problem. It increases the output and productivity. Decreased decision making time and downtime are the main aspect o beneit. Capture o scarce expertise, lexibility, easier equipment operation and ability to solve complex problems makes it more beneicial. Thus the system we propose here gives result depending upon user s preerences and also recommend a product based on other likeminded users is indeed an Intelligent Database Solver oriented Decision Support System (IDS-DSS). 3. ARCHITECTURE We propose a Decision Support System as a web application in electronic commerce to recommend products or e-shoppers. The Decision Support System would look like a product broker, which will ind a list o products based on user criteria through a desirable user interace that prompts shoppers to input their constraints and gets inormation though standard search procedures. Our system is composed o two agents-a Search Agent and a Recommender Agent. The irst is responsible or the generation o Cases based on the similarity measures; the second is responsible or proposing recommendation by using Rating based Item-to- Item collaborative Filtering. These two agents work and interact with each other. Our system is constructed on three-tier network architecture. Each tier has its own responsibility; together they orm a cohesive, lexible, and scalable shopping application. The irst tier o our application includes a browser. This is also called the presentation tier. Its main unction is to send messages to the middle tier by encoding commands and arguments in HTTP requests. The prototype o the system is created on a ASP.NET platorm, which supports several types o clients. ASP services can interact with their clients via dynamically generated ASPX pages and orms. The back-end tier structure o the system shows the similarity and case representation in SQL Database. The middle-tier application perorms the role o Recommender Agent and Search Agent. The ormer gets personalized content rom client browsers, then runs a task on behal o the user. The latter the agent can search any number o inormation sources to get a combined data that matches the selection criteria rom the user. The middle tier is the business tier, which is the heart o the system. The Search Agent is represented as a CBR mechanism, which uses a similarity engine to get mind-like searching results to the user s requirement. The intelligent Search Agent employs the user s preerence and constraints to sit through the inormation provided by numerous contents providers-especially limits at product descriptions in our system to deine relevant inormation. The inormation is aggregated by the Search Agent and is presented as a proile orm, which is then transerred to the client tier or publication. Figure 1. Schematic View o DSS

Figure 3. A view o the CBR problem solving cycle Figure 2. Architecture o the proposed system 4. PROPOSED ALGORITHM AND METHODOLOGY 4.1 Case-Based Reasoning (CBR) Case-based reasoning CBR is a multi-disciplinary subject that ocuses on the reuse o experiences ( i.e., cases). It is diicult to ind consensus on more detailed deinitions o CBR because it means dierent things to dierent groups o people. For example, consider its interpretation by the ollowing three groups[2]. (1)Cognitive Scientists: CBR is a plausible high-level model or cognitive processing. (2)Artiicial Intelligence Researchers: CBR is a computational paradigm or problem solving. (3)Expert System Practitioners: CBR is a design model or expert systems that can be used in either stand alone or embedded architectures. To introduce CBR requires identiying it in a particular context. For example, variants o Aamodt and Plaza s (1994) problemsolving cycle are requently used when introducing CBR to AI researchers. Figure below displays the view o ive top-level steps that, combined, input a problem description and output a (proposed) solution [3]. 1) Retrieve: Given a problem, retrieve a set o stored cases (e.g., <problem, solution, outcome> triplets) whose problems are judged as similar. In our case user preerence on multiple attribute are given and the resulting outcome are stored in the case-base with their respective recommendation. All recommendation is implicitly derived rom the user s action. 2) Reuse: Apply one or more solutions rom these retrieved cases, perhaps by combining them with each other or with other knowledge sources. 3) Revise: Adapt the retrieved solutions(s) as needed, in an attempt to solve the new problem. In our case we update the recommendations when the same product are retrieved again rom the case-base. 4) Review: Evaluate the outcomes, when applying the constructed solution to the current problem. I the outcome is not acceptable, then the solution will require urther revision. 5) Retain: Consider adding the new triplet o problem, revised solution, and outcome to the library as a new case. Thus the case base table or our system can be given as in Figure 4. Figure 4. Case-base structure It contains all the attributes o our entity which plays the role o a problem case as well as solutions to the problem. A recommendation parameter is also included that depicts the implicit rating given by the customer towards the product.

4.1.1 Similarity Measure in CBR The case structure object deines the hierarchy and cardinality o the eatures that can appear in a case. Within the case structure, there are a number o eature structures that deine the eatures that can appear in a case. These eature structures deines the eature s type, its value restrictions, and other attributes. The ollowing are a list o the possible eature types and the restrictions that can be imposed on them: Symbolic the eature value must be one o an enumerated list o possible values Numeric can be integer or double type (ranges can be set) Boolean the value can be either true or alse String the value can be any string Taxonomy similar to symbolic type except that the possible values are represented with a tree structure The similarity between a query, Q and a case, C is deined as the sum o the similarities o its constituent eatures multiplied by their relevance weights: Where SIM ( Q, C) w ( q, c ) F w is the eature relevance weight, is the local q is similarity measure (i.e. eature speciic similarity measure), the required th query attribute value and c is the value o the th attribute in the case base table and F is the number o attributes o our entity. In our case F =7. In order to provide a representation o the similarity measure it is thereore necessary to represent both a relevance weight and a description o the local similarity measure or every eature. Weights are just attributes o eatures but local similarity measures are more complex. We have deined our types o local similarity measures: Exact similarity measures - similarity is 1 i the eature values are equal, otherwise it is 0. Dierence based similarity measures Similarity is directly related to the dierence, δ between eature values. This measure is only suitable where a dierence can be deined between eature values, e.g. with numeric eatures the dierence is the mathematical dierence. Discrete similarity- The similarity values o some attributes are listed in a table; they are obtained rom a combination o similarities to various attributes. There are two kinds o tables: symmetric and asymmetric tables, according to the native eature o the dierent attribute values. The asymmetric attribute values dierentiate between a query attribute value o x and a case value o y, and vice versa. For example i the attribute is a book s rating and the user is looking or a book with three-stars, both a three-star and a ive-star will satisy the query (the similarity is 1). I the query is ive-stars, on the other hand, the three-stars book does not satisy the user s criterion. ( q Continuous similarity and adaptation- The similarity values o some attributes such as the product price are continuous. It can t be listed in a table like discrete attributes. In our IDS-DSS agent we describe the price o Laptop deined as in a unction shown below., c 1 ) ( c ~ q ) q where x 0 and x 1 are the threshold values. The array similarity measure is the best way to represent similarity. It is useul or eatures with a inite number o possible values, but requires the user to pre-calculate the similarities in advance. I a value cannot be ound in the array o similarities the exact similarity measure is used. The Table 1, shows a representation or the array similarity measure. Table 1. Table representing the array similarity measure RAM MEMORY Figure 5. Continuous Similarity measure x c x 0 1 None 512 MB 1 GB 2 GB None 1 0.8 0.4 0 512 MB 0.8 1 0.8 0.4 1 GB 0.4 0.8 1 0.8 2 GB 0 0.4 0.8 1 Local similarity unctions re-rank eature relevance (weight) based on the eature values o the probe cases. Thereore, it is possible to increase the eature relevance under certain conditions (user input) and decrease the relevance in other situations. In other words, the local weight may have to be changed depending on its task. Our methodology is essentially based on improvements resulting rom using a weighted combination o both short-term and longerterm preerences instead o using only short-term preerences. The mathematical deinition o the deiciency ( D ) is as ollows: D (1 ( q, c )) c c q q

In this equation, represents the relative importance o attribute q. There are quantities to be modiied when the user criticizes an attribute, which can be measured by a deiciency o the attribute. Once the deiciency o each parameter is computed, we cover one by one each parameter whose deiciency is higher than the criticized parameter, which we call D. ( D D ) D D Finally, the weight o the criticized parameter is more than at least one other attribute; i.e. the weight o the other attributes will be reduced. ( D D ) D D Thereore, there will have been at least one parameter whose deiciency is higher than the critical deiciency. When a user criticizes an attribute, we increase the weight o the attribute, which means that it is more important to him. Thus the cases are retrieved rom the case base depending on predetermined similarity criteria. 4.1.2 Pros and Cons o CBR Use o Case-based reasoning in the place o other alternative reasoning approaches gives raise to the ollowing pros and cons [2],. Incremental learning: Case-based approaches are suited or incremental learning tasks because they prevent the premature selection o summary abstractions. Training speed: Case-based approaches are usually much cheaper to use when storing inormation. Thus, CBR is preerable or tasks where storage speed is valued more highly than queryresponse speed, and/or when eicient caching mechanisms can be used to reduce the time to respond to subsequent inormation requests. Missing values: Lazy approaches, whether or use in a casebased, decision tree or other algorithm, are useul or tolerating missing values because lazy methods exist that require processing only the values known or the given query. However, CBR approaches have their limitation, they tend to have larger storage requirements, and demand attention or the deinition o case retrieval and adaptation. This storage problem can be checked by revising the case base, based on the recommendation. The cold-start problem can be compensated by getting rating o products rom the user during registration [5]. Thus CBR assures only accurate results whereas recommendations which are apart rom his preerences that has good rating rom others are not presented. Thus we go or a collaborative iltering approach or useul recommendations. 4.2 Collaborative Filtering The user-based collaborative iltering approach predict the opinion the user will have on the dierent items and be able to recommend the best items to each user based on: the user s previous likings and the opinions o other likeminded users. The traditional user-based collaborative iltering recommendation techniques proceed in these steps: (1) For a target/active user (the user to whom a recommendation has to be produced) the o his ratings is identiied (2) The users more similar to the target/active user (according to a similarity unction) are identiied (neighbour ormation) (3) The products bought by these similar users are identiied (4) For each one o these products a prediction - o the rating that would be given by the target user to the product is generated (5) Based on this predicted rating a set o top N products are recommended. 4.2.1 Issues in user-based collaborative iltering The recommendation produced by the traditional user-based collaborative iltering solely depends on the number o customers and when the number o users goes out o bound its becomes a time consuming and complex task to match the user to the similar customer. But the Item-to-Item collaborative iltering algorithm is based on similarity across items instead o a similarity across users which mean that we can sometimes do more precomputation and accommodate a larger number o users. Also, item-to-item algorithms are particularly suited or item similarity applications. 4.2.2 Item-To-Item Collaborative Filtering In the item-based collaborative iltering we compute the similarity between items and then to select the most similar items. The basic idea in similarity computation between two items i and j is to isolate the users who have rated both o these items and then to apply a similarity computation technique to determine the similarity between the user who have been rated or the same item. But the rating between the dierent user are not always the same i.e. one user might have a maximum rating o 10 whereas or the other user the maximum rating might be only 6. And hence by directly comparing the rating o dierent user we might result in a wrong recommendation. To solve the problem we shall go or the weighted slope one algorithm to normalize the rating o the users and urther enabling us to compare their rating. 4.2.3 Slope One Algorithm or Online Rating-Based CF Rating-based collaborative iltering is the process o predicting how a user would rate a given item rom other user ratings. We propose three related slope one schemes with predictors, which recomputed the average dierence between the ratings o one item and another or users who rated both. As shown in the diagram the dierence between items rated by the user will be ound. The Slope one algorithms are easy to implement, eicient to query, reasonably accurate, and they support both online queries and dynamic updates, which makes them good candidates or real-world systems [7]. Like Amazon s algorithm, ratingbased item-to-item algorithms are based on similarity across items instead o a similarity across users [6] which means that we can sometimes do more pre-computation and accommodate a larger number o users. Also, item-to-item algorithms are particularly suited or item similarity applications.

Table 3. Rating Dierence Table LaptopID Rating-Dierence ( ) L2002 1 L2003 1 L2003 0 L2003 1 L2004 4 L2005 2 Figure 6. User rating dierence using Slope One Let us consider a simple example or the explanation o the working o the algorithm. Table 2. Customer-Product Rating Table UserID LaptopID Rating DateTimeStamp C1001 L2001 4 BINARY C1001 L2002 5 BINARY C1001 L2003 6 BINARY C1001 L2004 5 BINARY C1001 L2005 6 BINARY C1002 L2001 6 BINARY C1002 L2003 7 BINARY C1002 L2004 9 BINARY C1002 L2005 8 BINARY C1003 L2002 6 BINARY C1003 L2003 7 BINARY C1003 L2001 5 BINARY The Table 2 shows how the input data are arranged in the database. And the last row represents the user who has just rated the laptop L2001, the target user is the user who has just rated the laptop and the laptop just rated by him is the target laptop. In our case C1003 is the target user and the laptop L2001 is the target laptop. And rom this we ind rating dierence or all other laptops to normalize the rating and avoid the possible errors. The Rating dierence ( ) o each laptop rated by dierent user except the target laptop. = R ( C ) R ( C ) where i j ; i, j User k i k j Where R ( C ) and R ( C ) reers to the k rating o the i k th k product. j th i & th j user We perorm the weighted slope one algorithm to obtain the popularity dierential between the target laptop and all other laptops in the Table 3. In the popularity dierential table, the irst time when we encounter a laptop pair we insert it and when it is once again encountered we update the record by increasing the counter value by one and the sum value increases with an addition actor o rating dierence. The top results o the popularity dierential table is Table 4. Popularity Dierential Table Target_Laptopid Other_Laptopid Count Sum L2001 L2004 1 4 L2001 L2005 1 2 Thus by the Table 4 we recommend the laptop L2004 or the target user C1003. The above discussed Weighted Slope One algorithm [7] meets the ollowing requirements. Easy to implement and maintain: all aggregated data should be easily interpreted by the average engineer; Updateable on the ly: the schemes should not rely exclusively on batch processing; Not demanding rom new users: as long as we can guess how a use eels about one item, we should immediately be able to oer recommendations; Eicient at query time: speed should not depend on the number o users and we should use extra storage as much as possible to pre-compute the queries; Other algorithms such as memory-based ones [1] do not allow us to pre-compute the recommendations and require that the user has rated several items beore a sensible recommendation can be made. Slope One is based on a simple popularity dierential which we compute by subtracting the average rating o the two items. 5. RESULTS AND DESCRIPTION 5.1 Experimental Study In order to illustrate our architecture and methodology, we constructed a DSS as a web application to give recommendations when users buy Laptops. Our prototype system s case base includes 300 cases or laptops against 700 laptops in the base table that are described by Brand, Processor, RAM, Hard Disk, OS, Monitor, DVD drives and Price. Those cases are made up o 6 Brands, 5 Processors, 5 RAMs, 5 Hard Disks, 2 OS, 3 Monitors, 3 Drives, 3 kinds o range price (such as $0-1000, 1000-2000, 2000-3000) and more than 300 pictures o those laptops. Each attribute's local similarities are deined between 0 and 1. All data were acquired rom http://www.laptoplogic.com/ which is available rom 2008-01-01. User preerences are ed

using drop down list boxes as shown in Figure 7 to avoid typographical errors. Options in the list boxes matches well with the attribute values in the base table so that the user is assured to ind laptops according to his preerences. Our archetype system s server has its business tier and back-end setup in a Pentium IV 2.4 GHz processing system with 1016 MB o RAM including 2446 MB o Page ile memory. Client systems require IE 4+ with a basic Dial-up/LAN connection with the server. The cold-start problem in CBR can be compensated by initially getting the rating o laptops rom the user as a part o the Figure 7. Module to get user preerences registration as in Figure 8. The laptops to be rated are presented based on time since it was rated and its recommendations. Figure 8. Module to overcome Cold Start problem As shown in Figure 9, the search agent o our system assures results at a aster rate that best matches the buyers preerences and the recommendation agent gives valid suggestions based on other like-minded user s preerences. 5.2 Analysis Figure 10 denotes that as the number o records in the case base increases the memory bounded CBR s case retrievals time increases gradually as shown. This can be overcome by updating the case base periodically. I the case base is not considered, the retrieval time increases linearly w.r.t the number o records. Figure 10. Access time Graph

Figure 11 depicts the variations o attribute weights in calculating the local similarity measure. The most criticized item (considered important to the user) is assigned more weight, whereas the least criticized item s weight is decreased to compensate the eect o weight modiication. Figure 11. Weight variation graph Figure 12 denotes that the Mean Absolute Error (MAE) o the recommendations produced decrease with the increase in the number o neighbour or the target user. Figure 12. Mean Absolute Error graph 6. CONCLUSION The incremental learning o the CBR prevents the premature selection o the cases and the cases retrieved are quick which makes it preerable or tasks where storage speed is highly valued. The cold start problem is overcome by initially getting the rating rom the user or a set o items. The collaborative iltering proposed here is easy to implement and updatable, which makes the recommendation produced highly eicient and since the algorithm is independent o number o user and the number o rating rom the single user the results produced are highly accurate. Figure 9. Results Module Thus we produce a novel paradigm that assures results at a aster rate that best matches the buyers preerences by predetermined similarity measured Case-based reasoning cases and give valid recommendations by rating based Collaborative Filtering method. 6.1 Future Works Building o the Decision Support System with Distributed Case-based reasoning. Implementing the system or the various classes o products. 6.2 Reerences [1] Gang Wu, Qiang Gong, Yu-Qiang Feng., Applying Case-based reasoning to multi-attribute E-purchasing decision,ieee, August 2005. [2] Aamodt, A., and Plaza, E., "Case-based reasoning:oundational issues methodological variations, and system approaches", AI Communications, Vol 17,No.1 pp.39-59,1994 [3] Aha, D.W., "The omnipresence o case-based reasoning in science and application" [4] Lorcan Coyle, Conor Hayes, Paidraig Cunningham., Representing cases or CBR in xml [5] http://movielens.umn.edu/login available by 2008-1-1. [6] Greg Linden, Brent Smith, and Jeremy York. Amazon.com recommendations: Item-to-item collaborative iltering. IEEE Internet Computing, January 2003. [7] Daniel Lemire and Anna Maclachlan. Slope one predictors or online rating-based collaborative iltering. In Proceedings o SIAM Data Mining (SDM 05), 2005 [8] Yanping Ma, Esma A imeur. Intelligent Agent in Electronic Commerce XMLFinder.IEEE, 2001. [9] Lorcan Coyle, Dónal Doyle and Pádraig Cunningham. Representing Similarity or CBR in XML.