Query Strategies Clustering Effect Acknowledgement Query Results Expansion on LDA Conclusions Contributions Introduction Experim
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Applying Topic Model in Context-Aware TV Programs Recommendation Jing Yuan, Andreas Lommatzsch and Mu Mu www.dai-labor.de Introduction Contributions Query Strategies In IPTV systems, users’ watching behavior is o Involving contextual factors in LDA model influenced by contextual factors like time of for TV programs recommendation to detect day, Live/VOD condition etc.. Yet modern the variation of users’ preference; context-aware recommending models (e.g. o Defining corresponding probabilistic query tensor) only consider general contextual strategies under specific algorithm and influences while overlook users’ reliance on watching scenario; various contextual factors. In this work, we o Conducting experiment on the data from a utilize Latent Dirichlet Allocation (LDA) to campus TV content delivery system overcome this issue. An extension to user- “Vision” in Lancaster University; oriented LDA is proposed by adding a probabilistic selection node in the o Representing the effect of the model w.r.t. probabilistic graphical model, so that both relevance and diversity; detecting users’ inclination on different o Visualizing the clustering effect on TV contextual factors is achieved. programs of this topic model. Expansion on LDA Query Results User Preference User Inclination on Topics on Factors The same probabilistic path that userId: 517 userId:135 o User-Oriented LDA these two approaches possess. The Simpsons UEFA Champions League Context For users (userId: 517) and The Big Bang Theory ITV News at Ten & Weather selection (userId:135), the programs BBC News ITV News & Weather recommended reflect their node You've Been Framed! BBC News different preferences. It’s u u The IT Crowd Football: England v Germany quite clear that user with Id The topic assigned Pointless UCL: Manchester City v Barcelona 517 prefers comedies and How I Met Your Mother Emmerdale to ith that program talk shows, while user with s 0 Take Me Out The Big Bang Theory user u has watched k c1 u,i s c2 Id 135 focuses more on u, i North West Tonight Football: England v Denmark news and sports. k [1, K] K Family Guy heute-journal s 1 tod u,i su ,i 2 type zu,i e z t o Context-Aware LDA u u,i u ,i userId:1105 t e u ,i Live/VoD path_lambda:[0.00025 0.9995 0.00025] time of day For user with Id 1105, N Time of Day u ,i i 1, Nu u Context Environment 9:00~12:00 18:00~21:00 her/his propensity on u [1,U ] U The Big Bang Theory The Big Bang Theory contextual factors is told by vu ,i BBC News Coronation Street vector path_lambda. The This Morning The Simpsons high value 0.9995 shows a A user The Jeremy Kyle Show Hollyoaks higher influence of “time of A transaction The ith Frasier Emmerdale day” factor than “live/vod” program user u or his/her usual preference. How I Met Your Mother How I Met Your Mother has watched k Rules of Engagement Come Dine with Me The table provides variant Top Gear EastEnders query result under different The Simpsons BBC News timing slots for this user. (1) User-Oriented (2) Context-Aware Experimental Results Clustering Effect (The lower the perplexity is, the better effect the model reaches.) o Converge on Perplexity Topic1 Topic2 Topic3 Topic4 Topic5 Topic6 Topic7 Topic8 Topic9 Topic10 The Big Bang How I Met Your Coronation Come Dine with Match of the Family Guy BBC News The Simpsons Top Gear I'm a Celebrity... Theory Mother Street Me Day New: How I I'm a Celebrity New: The Big New: Family ITV News & The Simpsons Supersize vs Best of Top Match of the Met Your Sportsday Get Me Out of Bang Theory Guy Weather Movie Superskinny Gear Day 2 For user-oriented LDA: Mother Here I Never Knew Double Your Big Momma's Ja'mie: Private The Tomorrow Top Gear: India The Football The Jonathan The Papers That About Accepted House for Half House School Girl People Special League Show Ross Show Britain the... The Martin The The Simpsons The Incredible FA Cup ITV News at Ten New Girl Sun Click Lewis Money Inbetweeners Dragons' Den Movie Hulk Highlights & Weather For context-aware LDA: Show Movie Welsh UEFA Sex and Come Dine I'm a Celebrity... New: The Call Adventure - Top Gear USA Champions 17 Again Suspicious HARDtalk Fresh Meat with Me Get Me Out of Centre Griff Rhys Special League: Extra Parents:... Christmas Here! Jones Time Rugby League: BBC News at A Great Welsh Up All Night: Top Gear Britain's Secret Suits EastEnders Bruce Almighty 37 Days World Cup Distribution of Ten Adventure Britain on Call Vietnam Special Treasures Highlights users’ inclination New: Rude Tube: Bad BBC News at Off the Beaten Harry's South Motorway on context. Cinderellas of Hairspray Secret Eaters Film 2014 Mystery Map Trips Six Track Pole Heroes Cops the Slums:... Disney's The Northumberlan Crocodile Great TV Top Gear Africa The Sky at I'm a Celebrity... o Relevance and Diversity New: Troy Fox and The The Film Review d with Robson The Repo Man Dundee II Mistakes Special Night Coming Out Hound Green National Big Fat Gypsy The Royal 2 Fast 2 The Island with Meet the The Sorcerer's Top Gear: Dads Television Christmas: The Cube Variety Furious Bear Grylls Author Apprentice Burma Special Awards 2014 Carols... Performance UEFA Horizon: A Great Welsh Would I Lie to The Big Bang The Budget Hairy Bikers' Top Gear Champions Swallowed by a Adventure with Disney's Holes The Jump You? Theory: It All... 2014 Cookbook Special League Sinkhole Griff... Highlights Context-Aware LDA with 200 topics, first 10 topics listed, 38 literally similar programs caught, similar program names are tinted within the same color inside each topic. Conclusions o We extend LDA by adding a probabilistic selection node such that either contextual Acknowledgement influence or users' inclination on different contextual factor can be learned. o We visualize the query result and clustering effect of TV program recommender The work of the first author has been funded by China Scholarship Council . under this probabilistic framework. Poster Demonstration @ KDML 2016 Workshop on Knowledge Discovery, Data Mining and Machine Learning in LWDA, Potsdam, Germany, 12th-14th Sept. 2016 .