Unifying Explicit and Implicit Feedback for Top-N Recommendation

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1 017 IEEE nd International Conference on Big Data Analysis Unifying Exlicit and Imlicit Feedback for To-N Recommendation Siing Liu, Xiaohan Tu and Renfa Li College of Comuter Science and Electronic Engineering Hunan University Changsha 41008, China {liusiing, tutu16103, Abstract In the era of big data, the data are diverse and comlex. The issue that using multi-source data efficiently in recommender system is very essential. To solve this roblem, we rooses a ranking model that integrates exlicit feedback data with imlicit feedback data together. We use weighting factors to measure the imact of different user behaviors on recommendation quality. We solved the data fusion roblem and the To-N items recommendation roblem. We used matrix decomosition for collaborative filtering. Finally, a arallel otimization model based on distributed and arallel comuting is roosed, and the imlementation on Sark is rovided. Through comarison with several models, our model greatly enhanced the items recommendation quality and imroved the scalability and efficiency of ersonalized recommender systems. Keywords-comonent: Recommender systems; Imlicit and exlicit feedback; Collaborative Filtering; To-N recommendation. I. INTRODUCTION Most of recommender systems focus on analyzing exlicit feedback information [3, 5, 6], while ignoring imlicit feedback [4, 0]. That does not effectively analyze the otential value of data and limits the develoment of recommender system. The roblem of integrated with exlicit and imlicit feedback fully for items recommendation, which arises from the actual recommender engineering alication in industry. The data source include Weibo, WeChat, web sites and so on, which include exlicit and imlicit feedback data tyes. Therefore, the study of how to use a large number of imlicit feedback data combined with exlicit feedback information for ersonalized recommendation is the core value of big data. The inut data tyes of the recommender systems include exlicit feedback and imlicit feedback data. Exlicit user feedback refers to the exlicit tendency given by users, such as movie ratings, roduct ratings and so on. However, imlicit feedback includes ointing raise or commenting in Weibo or WeChat, or browsing the Netease news web site, or buying goods in Taobao, etc. Comared with exlicit feedback, imlicit feedback data has a broader alication landscae and a larger data size, but is more difficult to integrate effectively. We know that imlicit rating data has the roblem of information redundancy and are difficult to summarize. The relationshi between data is not very tight, including multile data sources, and data may be heterogeneous. Imlicit feedback does not have negative feedback, that is, even if the user does not act it does not mean that users do not like the item. Imlicit feedback also contains a lot of noise. For examle, the browse behavior may be a age that the user oen the age by default which leads to a higher score. These cannot really reflect the user's references. The value of exlicit feedback indicates the user's real reference, while the imlicit feedback reflects the user's reference, and the value reflects the confidence. If you want to use recommendations that include imlicit feedback, you need an aroriate methodology for your assessment. In addition, the imlicit feedback has a big data size, usually coming from the database and server logs, which requires the recommendation model is efficient and has good scalability. For multi-source data roblems, our aer combines the imlicit feedback and exlicit feedback data to make ersonalized recommendation service. By transforming the recommendation task into the maximization roblem of the robability of user choice behavior, we can directly model the imlicit feedback data, which avoid the introduction of noise. By otimizing the user and the roduct's otential characteristic factor of the roduct we model the choice between the user and the roduct. Based on the distributed matrix and artition strategy, we roosed a arallel Unifying Exlicit and Imlicit Feedback Model (UEIFM) to imrove the scalability and execution efficiency. II. RELATED WORK The Collaborative Filtering (CF) recommendation algorithm based on matrix decomosition has a good erformance. Matrix decomosition technology [3] has gradually relaced the collaborative filtering algorithm based on neighborhood (user or object) to become the research hotsot. This is mainly due to the fact that matrix decomosition has higher rediction recision and better scalability. The robability matrix decomosition [6] reconstructs the scoring matrix by maing the user (roduct) into the low-dimensional sace and estimating the otential feature vector of the user (roduct). The dimensionality reduction technique is used to deal with high dimensional sarse data to imrove the rediction of recision. Bayesian Personalized Ranking (BPR) model has been roosed in [7]. By maximizing the robability of the ositive cases ranked in front of negative cases, the recommendation task was translated into a roblem of learning to rank, and alied to two common recommendation algorithms, namely adative KNN and /17/$ IEEE 35

2 matrix decomosition. The model was evaluated by a rivate data set of a web-based transaction on an online store and a samle of Netflix dataset. However, since the latter did not include imlicit feedback, the researchers converted the exlicit rating to the imlicit rating, and used the AUC as the evaluation measure [8]. We insired by the idea of simulation imlicit feedback. If a combination of exlicit and imlicit user feedback is used, the erformance of the recommended system will be imroved [1]. So far, algorithms suitable for combining exlicit and imlicit user feedback in the recommendation include matrix decomosition, linear and logistic regression [1, 13, 14]. Collaborative filtering using neighborhood models has not yet been adated to a combination of exlicit and imlicit user feedback. The arallel otimization algorithm of matrix decomosition is classified into two categories. One is the otimization method itself, and the other is the alicable distributed comuting environment. Stochastic Gradient Descent (SGD) is articularly effective in solving the matrix decomosition otimization roblems []. The imlementation is simle and efficient. Moreover, the comutational comlexity of each iteration is less. However, arallelizing SGD is a big challenge, because each ste descent deends on the revious udate which will cause overwriting roblems. When the matrix is very large and sarse, the overwriting roblem is more serious. The SGD overwriting efficiency is roosed in a number of revious aers [15, 16, 17, 18, 19]. DSGD [16, 17] block the matrix, by adjusting the udated sequence, to avoid the arallel udate which brings the overwriting conflict. FPSGD [18] further otimized cache and load balancing by using efficient access for sharing memory on the basis of DSGD, but there is a size limit that requires shared memory to be sufficient to fully load the feature matrix. The arallel otimization algorithm based on the MaReduce framework have been imlemented [16, 19], but the inherent overwriting roblem of SGD in arallel distributed environment still cannot be well resolved. Another matrix decomosition algorithm (Alternative Least Squares), can ensure the final convergence of the algorithm by alternately fixing one of the otential feature matrices and otimizing another otential feature matrix. Aiming at the high comutational comlexity of each iteration of ALS, a cyclic coordinate descent (CCD) method was roosed [9], which only udated one variable at a time on the basis of fixing other variables. On this basis, CCD++ is roosed [10], which further reduces the comutational comlexity by changing the sequence of variable udating. The arallel otimization algorithm mentioned above in the dislay feedback recommendation has been well verified. For imlicit feedback, the arallelism is to aroach the otimization objective (least squares error sum) of the traditional matrix decomosition as faster as ossible. The arallelization otimization method roosed in this aer combines the dislay and imlicit feedback recommendation models, imlements the related algorithms based on Aache Sark framework [1], and imroves the rediction accuracy of the recommender system. III. THE PARALLEL UEIFM AND THE SPARK PLATFORM IMPLEMENTATION In the era of big data, data are diverse and naturally formed. The massive data are often structured, but sometimes are semi-structured or unstructured. To achieve the recision of the recommendation, data fusion is an indisensable ste, which make full use of the diversity of the multile data. Our aer integrates multi-source data and constructs a new ranking model based on the integration of exlicit feedback and imlicit feedback data. Because of the large scale of data which requires the recommendation model to be sufficiently efficient and scalable. Then a arallel Unifying Exlicit and Imlicit Feedback Model (UEIFM) is designed and imlemented in the distributed comuting framework. A. UEIFM According to the data from Weibo or WeChat for ointing raise or commenting, browsing the web sites, databases data and logs data, we introduce the data weight, which reflects the information quality of each data source. Suosing there are m exlicit data sources, the overall exlicit weight is s, the k -th data weight is k, the k -th exlicit score is E k, the number of imlicit data sources is n and the imlicit weight is t. The data weight of item v is q v. The imlicit score of item v is I v. We get one value R ij of the rating data matrix as follows: m R s E t q I ij k k v v k1 v1 n (1) Number of k score items of user i th k () q v Average number of score items available to the score grou Number of data items for the vth behavior of user i Average number of data items available to the behavior grou (3) Many values like R ij form a rating matrix. The resulting rating matrix is very large and sarse. Pave the way for the following arallelization, we choose the coordinate matrix on Sark, and use the rogramming model of Resilient Distributed Dataset (RDD). The essence of RDD is a customizable arallel data container. Different dataset formats corresond to different tyes of RDD. We create a distributed matrix named CoordinateRowMatrix with the MatrixEntry instance. Each term is as a tule of row and column values in the form (i: Long, j: Long, value: Double), where i is the row coordinate value, j is the column coordinate value, and value is the rating score. Then transform the tule into MatrixEntry (Long, Long, Double), which facilitates distributed arallel comuting. The otimization of our objective function is as follows: ) F c R x y x y (4) (X,Y) min ui ( ui u i ) ( u i ) x, y ui, u i 36

3 cui is the credibility, the weight of evaluation that user u values item i. Rui is the value of the user-item matrix R, reresenting the user u whose evaluation on the item i. is the regularization arameter, which controls the degree of regularization, and used to avoid over-fitting roblems. x u reresents the feature vector of user u, yi denotes the feature vector of item i. Due to the large amount of data, the stochastic gradient descent method is no longer suitable. Otherwise the ALS is designed to find two low dimensional matrix X ( m* k ) and matrix Y ( k *n ). To aroximate the aroximation R ( m* n), we use the arallel ALS method to get the value ste by ste with iterative aroximation. u F( X, Y) 0 X 1 ( u ) u ( ) (5) X Y C Y I Y C R u (6) The method to get Y i is similar to X u, Ru ( ) is the value of rating matrix we get from function (1). R X Y (7) * T ui u i The rogress of our algorithm is as follows: 1) Calculate the function (5). ) Calculate the function (6). 3) Make the derivative of Y i to be equal 0, then get the value of Y i as above. 4) Practice as the ste 1), ), 3). 5) The X u and Y i vectors are alternately determined and the other is calculated so that the value of the consumtion function is reduced. The rocess above is reeated until the value is convergent or stable. 6) R ui is calculated from function (7). B. Parallel Imlementation of UEIFM on Sark Due to our UEIFM is mainly the iterative udate rocess, in which the fixed x u to udate the y i, or the fixed y i to udate x u. The seudocode of inut is as follows: Inut: F as Number of features. ITERATIONS as the number of iterations. LAMBDA as Regularization coefficient. The exlicit and imlicit user feedback datasets. Outut: User matrix and item factors of features. 1) Calculate R as Grade CoordinateMatrix (Distributed Matrix) by RDD[MatrixEntry] data,because the original tye is tule (long, long, double), convert to MatrixEntry (long, long, double). ) Initialize U F as ms and F M as us matrix randomly. Due to the block size and artitioner,we can get the iteminblocks and userinblocks reectly. 3) Sarkcontext broadcast R and ms and um to all the Executor of s. 4) Iteratively udate items then users, Fixed M, one by one to udate each feature of each user, so that the artial derivative is equal to 0; and then fixed U, one by one to udate each item of each feature, so that the artial derivative is equal to 0. Then comute comute RMSE by user features and item features. 5) Then udate user or item rocess, Solve it with Cholesky solver for least square roblems with L regularization. 6) Finally, get the result of userfacotrs and itemfactors. IV. EXPERIMENT AND EVALUATION A. Exerimental Environment We use the HP-workstation comuter in our lab as exerimental environment. The comuter is configured to 55G memory, 1 cores CPU. We use the lightweight virtualization technology Docker [3], and then create containers over the Ubuntu host OS. Finally, the deloyment of 5-node Sark cluster are ready. We use HDFS (Hadoo Distributed File System) to store exerimental data, and virtual network adater docker0 to establish network connection. A cloud comuting latform for distributed arallel comuting has been built. Master 1 Docker Engine Ubuntu(Host OS) 3 Figure 1. The exerimental environment. 4 B. Exerimental Dataset We used MovieLens [1] as the exerimental datasets to simulate exlicit ratings and imlicit feedback data when comuting the ranking, which includes Movielens-1m, Movielens-10m, and Movielens-0m. Taking one of them as an examle, Movielens-1m contains ratings of 3883 movies and 6040 users. The user number is from 1 to 6040, and movie number is from 1 to 395, but only 706 movies are actually rated. Not all items are rated. The user number and movie number are not continuous, and a number reresents a single user. A single user rates 0 movies at least. The rating density is 4.47%. Details are shown as Table I. 37

4 TABLE I. EXPERIMENTAL MOVIELENS DATASETS datasets users items(actual) ratings density 1m 10m 0m (3706) % (10677) % (6744) % The total number of Movielens datasets s ratings is After calculating, the distribution of ratings are as shown in Table II. We can see that rating score 4 is the most, and second is rating score 3.The lower rating score is much less than the higher rating score. TABLE II. RATING DISTRIBUTION rating numbers Ratio % % % % % % % % % % C. Exeriment Evaluation After the fusion of exlicit feedback and imlicit feedback data, we train the recommendation model, then get the To-N recommendation. To-N recommended recision is generally measured by the recision rate, recall rate [8] and so on. So in our aer, we mainly calculated the rec@n (recision at N), recall@n (Recall at N), and a few additional auxiliary evaluation indicators, which are AUC (Area Under the ROC curve), MAP (Mean Average Precision), which calculate the average recision at all N value, And the NDCG (Normalized Discounted Cumulative Gain). Finally, we calculated the F 1 score. According to our model UEIFM (the integration of the user's exlicit feedback and imlicit feedback data), we make recommendation lists as Ru ( ) for the user, and use the test set to get the test item lists as Tu. ( ) The recision of the recommendation results is defined as: Precision uu Ru ( ) Tu ( ) Ru ( ) uu The Recall for recommendation results are defined as: Recall uu Ru ( ) Tu ( ) uu Tu ( ) (8) (9) The average AUC is usually defined as in [7]: 1 AUC AUC( u) U u (10) U The discounted CG accumulated at a articular rank osition is defined as: reli DCG i1 log ( i 1) (11) And then, NDCG is comuted as: NDCG The F 1 score defined as: DCG (1) IDCG recision recall F 1 recision recall (13) D. Exeriment Results We comare our model UEIFM with ItemKNN [], UserKNN, WRMF [4], and BPRMF [7]. We use MyMediaLite [], which has imlemented some of items recommendation model and evaluated by 5-fold cross validation. It means that the exerimental datasets was divided into training data set and test data set by 0.8 to 0.. We followed to imlement the evaluation metrics. The results are shown in Table III and Table IV. Our UEIFM model got the recision at to-5 and to-10 are and resectively. The recall value of our model are and In the comarison of recision, both rec@5 and rec@10 are better than WRMF AND BPRMF. In the comarison of recall@5 and reacall@10, there is a more significant reduction than WRMF and BPRMF. We are concerned with the recommendation recision, not the recommended relevance, because we get a lot of user-related recommendations, but we only care about the revious To-N recommendations such as 1, 5, or 10. Therefore, we got high recision and low recall. TABLE III. PRECISION AND RECALL COMPARISON Model rec@5 rec@10 recall@5 recall@10 ItemKNN UserKNN WRMF BPRMF UEIFM By using the advantages of using exlicit and imlicit feedback data, the AUC value of our model is , which is better than that of WRMF. Our model s NDCG value is , which is better than WRMF and BPRMF, due to suerimosing the results of calculations on To-5 and To-10. AUC and NDCG showed that the UEIFM is 38

5 ossible to recommend the user's favorite item with a greater robability. But the MAP is too low as , which calculate the all recision. The F 1 score takes into account the recision and recall of the model. F 1 score that the recall and recision are equally imortant, but our model condsider that the recision is more imortant, so the F 1 score is less than other models. TABLE IV. AUC, MAP,NDCG AND F1 SCORE COMPARISON Model AUC MAP NDCG F1 Score ItemKNN UserKNN WRMF BPRMF UEIFM V. CONCLUSION AND FUTURE WORK Based on the integration of exlicit feedback and imlicit feedback data, a new model UEIFM is constructed and imlemented by the distributed comuting framework of Sark on cloud comuting technology, and then we get the To-N recommendation by ranking. We comared our model with ItemKNN [], UserKNN, WRMF [4] and BPRMF [7]. Whether in recision or recall, our model is relatively better. The value of AUC is better than WRMF. Due to the distributed comuting framework and cloud comuting environment, our model is more sufficiently scalable and efficiency. Because of the combination of multile exerimental datasets, the enhancement erformance in the exlicit and imlicit feedback data is reduced. In future work, we need to consider more about the imrovement of recision erformance, and increase the versatility of our model. ACKNOWLEDGMENT This aer was suorted by the National Natural Science Foundation of China (No: and 61089), the Science and Technology Plan of Hunan Province (No. 015GK3015) and The National High-Tech Research and Develoment Plan of China under Grant (No.01AA01A301-01). 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