User Tweets based Genre Prediction and Movie Recommendation using LSI and SVD

Transcription

1 User Tweets based Genre Predcton and Move Recommendaton usng LSI and SVD Saksh Bansal, Chetna Gupta Department of CSE/IT Jaypee Insttute of Informaton Technology,sec-62 Noda, Inda Anuja Arora Department of CSE/IT Jaypee Insttute of Informaton Technology,sec-62 Noda, Inda Abstract The emergng popularty and rase n users posts on socal meda gave brth to numerous research challenges. Out of all challenges users centrc context nformaton based recommendaton s one prme research area to recommend jobs, events and moves. Here n ths research work we focus on move context aware recommendaton and for ths purpose, we analyze users posted move tweets to understand ther ntentons for the same. Therefore, the objectve of ths research work s to predct genre of moves based on user s posted move tweets and recommendng moves to users accordng to predcted genre. For ths purpose, we pre-processed twtter extracted move tweets usng tokenzaton, porter stemmng, stop word removal and use Word-Net dctonary for synonym matchng. Further, we apply Latent Semantc Indexng technque whch n turn nvolves Sngular Value Decomposton on ths pre-processed data and predcts genre on the bass of IMDb move genre categorzaton. The predcted genre conveys the move nterest of the user and to recommend move on the bass of predcted genre whch s measured through eucldean dstance. We have extracted IMdb gven move data and further predcted genre usng our proposed technque. To valdate ths we dvded our dataset usng pareto prncple and matched wth IMDb gven genre data set and acheved approxmate 70% accuracy usng our approach. Keywords context aware recommendaton, Latent Semantc Analyss(LSI), Move recommendaton, Move genre predcton, Tweets, Twtter, SVD I. INTRODUCTION AND BACKGROUND Recommender systems are operatonal nformaton flterng systems whch flter tems accordng to user nterest/ ntentons. Nowadays, mmense amount of moves are avalable correspondng to every genre. Preference/ nterest of move and genre vary from person to person. It becomes really cumbersome for a person to fnd move of hs concern from the large number of avalable optons. An effcacous Move Recommender System has managed to address ths ssue. Much work has been reported usng the LSI technque whch nvolves topc classfcaton from documents [2-6]. Sona Bergamasch and Laura Po[2] have proposed a plot-based recommendaton system whch s based on evaluaton of smlarty between plot of user-purchased vdeo and the plots of vdeos stored n database. They have mplemented and compared the two topc models LSI and LDA on the large database. The performance of both technques s computed and LSI proves to be more superor. Ka Chen et. al.[3] proposed a framework usng text category method to classfy musc genres. LSI technque has been used by them for musc genre classfcaton. They have acheved a 70.% of correct classfcaton usng the proposed technque whch comes out to be better than the popular musc genre classfcaton such as GMM[9]. Suvr Bhargav [4] has proposed to extract move related features such as descrpton of locaton, characters and genres from user wrtten move revews. He has used LSI and LDA for the feature extracton from the move revews. Smlarty metrcs such as Hellnger dstance s then used to fnd moves wth smlar topc dstrbuton. Badrul M. Sarwar, George Karyps, Joseph A. Konstan and John T. Redl [5] have expermented wth two dfferent technologes for the recommender systems databases. Frstly, SVD has been explored to reduce the dmensonalty of the database and to recommend user preference tems based on a database of explct ratng of products Secondly, they have compared the nfluence of two recommender systems at producng Top-N lsts based on a database from an e-commerce ste. SVD was found better on the database that was based on real consumer purchase. Thomas L. Grffths, Mark Steyvers and Irvne Joshua B. Tenenbaum [6] analyzes the abstract computatonal problem underlyng the extracton and use of gst, formulatng ths problem as a ratonal statstcal nference. Ths leads to a novel approach to semantc representaton n whch word meanngs are represented n terms of a set of probablstc topcs. The topc model performs well n predctng word assocaton and the effects of semantc assocaton and ambguty on a varety of language-processng and memory tasks. The focus of our paper s to provde an automatc move recommendaton system that s based on semantc understandng of lve tweets beng scraped through twtter API [0]. Recent studes also clam the effect of move tweets on user s decson to watch a move, 87% of the people sad that move tweets on twtter nfluence ther recent move choce []. In ths research work, all the tweets related to the favourte move of the user are fetched from Twtter and then the semantc analyss of the tweets starts through whch s used to predct genres of the move. We explored the topc modellng technque and appled latent semantc ndexng (LSI) [] technque for twtter-based recommendaton system. Although the topc model s not new n the feld of recommendaton system but use has not been /6/$ IEEE

2 deeper analyzed n user tweets based recommendaton for moves. Therefore, the tweet based analyss usng LSI n ths doman gves novelty to our research work. The major focus s on the fact that we have not used any user context nformaton for recommendaton, our man crtera to nterpret the move nterest of the user s on the bass of move tweets posted by other users at twtter. Our recommender system asks for user s sngle favourte move, fetches move tweets for the same and fnally map and measure genre based smlarty ndex of favourte one wth enormous amount of move tweets avalable n database stored n Mongodb. System dentfes genre of all moves whose descrpton s already n database based on ther tweets and map t wth favourte move tweets generated genre to predct and recommend most mapped moves. The rest of the paper s structured as: Proposed genre predcton and genre based recommendaton model and ts process flow dscuss n secton 2. Secton 3 descrbes the descrptve statstcs of used dataset. Secton 4 s about methodology used and t contans varous sub sectons correspondng to genre predcton and move recommendaton from fetched move tweets. Secton 5 dscussed results and fnally concludng remarks n secton 6. components to recommend wth respect to users past preferences- Neghbor dentfcaton means users smlar to the target user; personalze preference on the bass of preference pattern. Whereas, t s also a pont of thought, f we don t have any prevously stored user data then how to analyze user s preference. Our work s bascally a solvent approach for ths sort of scenaro. In ths research work, we apply collaboratng flterng on the bass of component parameters/ propertes. For ths purpose, we ntroduce the projected tweet based genre predcton and move recommendaton model shown n fgure and Flow Dagram of projected approach s shown n fgure2. Frst and second stage of projected model s data extracton stage shown n dotted lne crcle n fgure. Ths data extracton stage educes nterestng and enormous amount of move tweets from twtter usng twtter API [9] and store extracted move tweets n MongoDb. Another requred data s IMDb genre keyword dctonary [7] and IMDb move data. Here we elct genre keyword dctonary from IMDb through ts feature of keyword search and to mprove t we collect genre assocated keywords or synonym words from Wordnet dctonary [2] Fg.. Tweet based genre predcton and recommendaton model II. TWEET BASED GENRE PREDICTION AND MOVIE RECOMMENDATION MODEL Collaboratve flterng s a related feld of research whch covers the relatonshp between users and tem. The prme objectve of collaboratve flterng s to recommend and predct users preferences on the bass of prevously rated tem by users. Basc collaboratve flterng has two sgnfcant Thrd stage s Pre-processng of tweets to reduce data dmenson to bump off unnecessary and waste data. To pre-process twtter collected tweets data, we use flterng to remove stop word, nconsstent data. Thereafter, we apply porter stemmng approach whch s a natural language processng technque to dentfy root node such as word processng wll reduce to root word process.

3 Fourth and ffth stages are bascally ntegrated stage. Fourth stage s Genre Dctonary Vectorzaton and Formaton of bag of words whch use vector space model (VSM) to generate a vector space n whch both document and queres are represented by vectors. Here documents are pre-processed tweets and queres are genre keyword dctonary words. Ffth stage s Formaton of query matrx s bascally matrx of tweet keywords and ther collgated genre whch s bascally a boolean sort of matrx based on keyword matches wth genre means or not match means 0. Sxth stage s Matrx Reducton as formed term-document matrx s of hgh dmenson. Therefore to overcome ths ssue we apply Latent semantc ndexng usng sngular value decomposton to reduce dmensons. Fnal last two stages Genre Predcton and Move recommendaton are result stages. For genre predcton, our system desres a sngle preference of user s choce move whch s our query and wth the help of ffth stage system wll generate a query matrx accordng to keyword les n query tweets. System follows all projected model steps for user preferred move query- extract preferred move tweets, pre-process tweets, run t on formed query matrx and on the bass of smlarty measure predct preferred genre and recommend move of more smlarty value to user. extracted data accordng to our need. Here n ths work, we bascally use three datasets, two from IMDb ste and one move tweets dataset from twtter. Frst Dataset s IMDb extracted genre dctonary whch s bascally a keywords dctonary correspondng to genre gven at IMDb. For ths, we fetched and stored genre related keyword from IMDb webste usng python lbrary- BeautfulSoup [3]. BeautfulSoup s a Python package used for parsng HTML and XML documents. To mprove dctonary data, we used wordnet [] for Synonyms. Genre dctonary data set detals are mentoned n TableI. Second Dataset used n the project s collected from Twtter. Twtter s the most wdely used socal networkng ste and t gves us real tme data and can be used n research to produce results wth relevance to the real world. The database of tweets s created usng Twtter API- Tweepy[4]. Detaled dataset nformaton reflected n Table I. Thrd Data set used n ths research work was IMDb move dataset shown n Table I whch s dfferent than prevously mentoned IMDb genre dataset. Bascally we used ths dataset to valdate our result. In our work as we predct genre therefore to valdate predcted genre, we extract few IMDb move data whch we matched wth twtter extracted move data. Thereafter on the bass of matchng we determne accuracy of resultant predcted genre. TABLE I. DESCRIPTIVE DATA STATISTICS OF USED DATASET IMDb Genre Dataset ~ 0 number of Genres: Acton, Anmaton, Comedy, Fantasy, Horror, Romance, Scf, Sport, Thrller, and War. Mean # of Genre Keyword 23.7 Medan # of Genre Keywords 26.5 Max # of Genre Keywords 36(n acton) Twtter Data Set ~ 00 number of Moves tweets extracted ~5000 tweets Average length of tweets: 40 words IMDb Move Data set Number of Moves: 20 (20% of twtter data set move) # of genre n each move:2 Fg. 2. Flow Dagram Of Proposed Approach III. DESCRIPTIVE STATISTICS OF USED DATASET For experment, we extract and crawl dataset from IMDb and Twtter. We have used IMDb for move dataset and twtter has been used for users tweets assocated to moves. Snce our nterest s n mappng and fndng correlaton among moves on the bass of users posted move tweets. Thus we IV. METHODOLOGY A. Pre-processng of Tweets For text processng on tweets we used NLTK, an open source lbrary [5]. The move tweets are extracted, then only the text part of the tweets s kept by applyng varous text clearng technques. Text clearng technques are requred because n-correct or nconsstent data can lead to false conclusons and msdrected nvestments on both publc and prvate scales. Frstly, all the urls, hashtags, punctuatons are removed from tweets. Secondly, the less relevant words also called the stop words are removed from the tweets. We used default stop word of englsh language. Thereafter, the stemmng technque called the Porter Stemmer stemmng s appled to convert the words nto stems (root word). All ths s easly acheved through the process of NLP. Text flterng helps n removng unnecessary nformaton. The fnal

4 keywords obtaned after pre-processng of tweets are stored n an array. B. Genre dctonary Vectorsaton and Formaton of Bag of words Keywords of each genre crawled from IMDB are stored n the database. Each genre s consdered as document and term are consdered as keyword assocated wth each genre contaned n genre dctonary shown n Table II. We represent ths structure nto vector space model [6]. Each word corresponds to a dmenson and each document s a vector wth non-negatve values on each dmenson. To represent document- term as a vector, consder each word as a term. The keywords of varous genres are then fetched from the database such as all the words from acton dctonary are fetched and then are assgned the value correspondng to acton column and all other columns are assgned value 0 ndcatng that ths keyword belongs to only acton genre. Then all the keywords from anmaton dctonary are fetched and are compared wth the prevously added terms n bag of words. If the term matches then t s gven a value correspondng to anmaton column representng that ths keyword represents both acton and anmaton genre. If the term does not match then t s added to the bag of words and s assgned a value correspondng to anmaton column and rest all columns are gven 0 value. Ths process s repeated for all the ten genres and fnally a m x dmenson s formed where m represents the total number of terms. Frst column represents the keyword and other 0 columns represent the genres. Pseudocode for the same s shown n fgure 3. TABLE II. REPRESENTATION OF A PART OF BAG OF WORDS FORMED Acton Anmaton Comedy Fantasy Horror Romance Sc-f Sport Thrller War warror betray terror detect battle sabotage teleport Basket ball Pseudocode: Fetch keyword from database Check f the word s already present n the bag If word present n bag of words: Update correspondng genres s value to else: Add the word to the bag of words. Mark the correspondng genre s value to and set all other genres to 0. Fg. 3. Pseudo code for bag of words generaton C. Formaton of query matrx All prevous steps are fundamentally assocated keyword-genre mappng matrx generaton steps. For predcton and recommendaton, Our approach demands for a sngle preference move from user whch s bascally our query. System extracts tweets assocated to query term and obtans array of keywords after pre-processng of tweets whch are used to create a query matrx. Extracted tweets for a sample move as query d shown n fgure4. The keywords from bag of words are compared wth all the array of keywords of pre-processed tweets. If the word matches then value s marked else t s marked 0. Fg. 4. Twtter Extracted tweet content D. Matrx reducton by topc model The above formed term-document matrx s of hgh dmenson. Therefore, to tackle ths problem latent semantc ndexng (LSI) has been appled whch uses sngular value decomposton on document term matrx to reduce the matrx dmenson.

5 Latent Semantc Indexng (LSI) s a model for extractng and representng the contextual-usage meanng of words by statstcal computatons appled to a large corpus of documents. The LSI conssts of a Sngular Value Decomposton (SVD) of the matrx T (Tranng set matrx) followed by a Rank lowerng.the orgnal matrx bag s genre-keyword matrx and represents the relatonshps between keywords and genres. The SVD conssts of representng the matrx bag as the product of three matrces as n equaton: bag=u. S. V... () Here, we reduced bag matrx to three matrces U, S and V where U and V are orthogonal matrces and S s a dagonal matrx n SVD process. The pseudo code of appled SVD s shown n fgure 5. The SVD of the matrx s consequently followed by a Rank 2 approxmaton by keepng the frst two columns of U and V and the frst two columns and rows of S whch gves us U, V,S. The columns of V gves us the new vector coordnates of varous genres n reduced 2-D space The purpose of dmensonalty reducton s to reduce nose n the latent space, resultng n a rcher word relatonshp structure that reveals latent semantcs present n the collecton.then the query vector coordnates n reduced 2-dmensonal space are found wth the help of the equaton 2: query = query. U. nv(s)... (2). bag U.S.V 2. U bag.bag 3. Egen values and egen vectors of U are computed. 4. Orthogonal matrx of egen vector U s computed. 5. V bag.bag 6. Steps 3 and 4 are repeated for computaton of V 7. V V 8. S dagonal matrx of egen values. 9. For every genre vector: Egen values n every row of V 0. query query.u.nv(s) Fg. 5. Pseudo code for Matrx reducton usng LSI E. Genre predcton and Move recommendaton ) Genre Predcton: The predcton of genre of move s done on the bass of smlarty between the varous document and query vector. The cosne smlarty metrcs used to measure smlarty. It s the most used measure of document smlarty. Its usage can be seen n nformaton retreval doman. In order to measure the smlarty of genre wth the query matrx we can calculate the cosne of the angle between the two term vectors (equaton 3, 4). A. B Smlarty= cos(θ ) = = A B = n n = A A 2 B n = B 2... (3) Here A s the document vector representng varous genres and B s the query vector. The smlarty of varous genres wth the query vector was calculated and they were ranked n decreasng order gvng us the three predcted genres of the move as shown n fgure 6. query. genre Smlarty (query, genre) =... (4) query genre 2) Move Recommendaton: The moves are recommended by usng the concept of content based recommendaton system whch calculates the dstance between the objects to compute ther smlarty. The less the dstance between the objects, more smlar they are to each other. The Eucldean dstance of the target move s calculated wth the moves already stored n the database. The smlarty s calculated for only those moves whose all three or two or one genres match wth the genres of the target move. Fnally the top fve moves are recommended as shown n fgure 6. Predcted Genre Recommended Moves Fg. 6. Predcted Genre and Move recommendaton Result V. RESULTS We have used Pareto Prncple whch s also known as rule for the verfcaton of our genres whch are beng predcted through our projected approach. Accordng to the Pareto Prncple we are takng 20% move data of the tranng set and assgned genre to move exst n test set. To valdate and verfy tweet based predcted genre accuracy, collects IMDb assgned genre of test set moves and compare the predcted genres wth the IMDb assgned genre. The accuracy acheved by projected tweet based genre predcton approach s 70%. Same matchng s shown n table III.

6 TABLE III. PREDICTED GENRE AND IMDB GENRE COMPARISON VI. CONCLUSION AND FUTURE WORK In ths work a twtter based genre predcton and move recommendaton system was developed. The system targets the move related tweets posted by users to dentfy ther preferred genre. Tweets were preprocessed to fnd the relevant keywords and then they were compared to the genre keyword s of varous genres. Genre Predcton was acheved wth the help of LSI and SVD. User was recommended moves of hs preferred genre by calculatng Eucldean Dstance wth the moves already stored n the database.the focus was to mprove the qualty of move recommendaton accordng to the genre predcton of the move. There s a lot of scope n semantc analyss of user posted move related tweets n recommendaton of moves.in long term ths system can be extended to nclude varous other features of move as well lke ratng, star cast, songs etc. [5] Sarwar, B., Karyps, G., Konstan, J., & Redl, J. (2000). Applcaton of dmensonalty reducton n recommender system-a case study (No. TR ). Mnnesota Unv. Mnneapols Dept of Computer Scence. [6] Grffths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topcs n semantc representaton. Psychologcal revew, 4(2), 2. [7] [8] [9] [0] [] [2] [3] [4] [5] [6] Katta, E., & Arora, A. (205, August). An mproved approach to Englsh-Hnd based Cross Language Informaton Retreval system. In Contemporary Computng (IC3), 205 Eghth Internatonal Conference on (pp ). IEEE. REFERENCES [] Latent Semantc Analyss and Topc Modelng: Roads to Text Meanng H Tú B o Japan Advanced Insttute of Scence and Technology Vetnamese Academy of Scence and Technology [2] Bergamasch, S., & Po, L. (204). Comparng LDA and LSA Topc Models for Content-Based Move Recommendaton Systems. In Web Informaton Systems and Technologes (pp ). Sprnger Internatonal Publshng. [3] Chen, K., Gao, S., Zhu, Y., & Sun, Q. (2006, October). Musc genres classfcaton usng text categorzaton method. In Multmeda Sgnal Processng, 2006 IEEE 8th Workshop on (pp ). IEEE. [4] Bhargav, Suvr. "Effcent Features for Move Recommendaton Systems." (204).