TAPER: A Contextual Tensor- Based Approach for Personalized Expert Recommendation

Transcription

1 TAPER: A Contextual Tensor- Based Approach for Personalized Expert Recommendation Hancheng Ge, James Caverlee and Haokai Lu Department of Computer Science and Engineering Texas A&M University, USA ACM RecSys 16:: September 18th, 2016

2 Recommender Systems Items (Movies, Songs, News, etc.) High-quality Content Producers

3 OUR GOAL: Recommend these experts to the right people High-Quality Content Producers (Experts) Personalized Expert Recommendation

4 Politics Technology Entertainment 1? 1? 1? 1 1? 1?? 1 1??? ??? 1 1 Users?????? 1 1 1????????? 1?? 1?????????? 1???????????????? 1?????????????? 1???????? 1 1? 1??????????? ??????? 1 1?????????????????????????????????? Topics Experts User-Expert-Topic preferences can be represented as a Tensor

5 Basic Tensor-based Personalized Expert Recommendation Matrix factorization CANNOT simultaneously consider all dimensions. Input: observed tensor, indicator tensor. Output: complete tensor X, latent matrices U (1),U (2),U (3). Minimize U (n),x Experts T 1 3X 2 kx [U (1),U (2),U (3) ]k 2 F + 2 subject to X = T,U (n) 0,n=1, 2, 3 n=1 ku (n) k 2 F, U 1 (3) U 2 (3) U R (3) U 1 (2) U 2 (2) U R (2) Users U 1 (1) U 2 (1) U R (1)

6 Challenges 1. Personal Experts 2. Sparsity 3. Complex Relationships David Jane Amy Ryan

7 Idea: Use Contextual Information Social Activities Temporal Q1: How to model? Q2: How to integrate? Location Topics Q3: Which is more important?

8 But First: Geo-tagged Twitter Lists A curated group of Twitter accounts. Allowing a user to label another user with an annotation (e.g., tech). List creators as users and members in the lists as experts Tech Food

9 Geo-Spatial Context Ryan CDF Atlanta Chicago Dallas Denver Houston Seattle San Francisco Washington DC Distance CDF of the average distance bet. users and experts by locations Jane David The geo-spatial context does affect the preference for experts with varying degrees based on topics and locations.

10 Topic Context 0.8 Entertainment Food Ryan Cosine Similarity Number of Shared Topics # Shared Topics VS. Similarity between Users Technology Technology Entertainment Entertainment Politics Jane David Politics The topic context does affect the preference for experts.

11 Social Context Ryan CDF Social Ties 0.2 users who follow the other users who do not follow Cosine Similarity between Users Jane David The social context does affect the preference for experts.

12 Modeling Contextual Preferences Bet. Homogeneous Entities Similarity Matrix S Bet. Heterogeneous Entities Users Adjacency Matrix A Adjacency Matrix AB Experts Topics Adjacency Matrix AC Similarity Matrix S Similarity Matrix S

13 Contextual Tensor-based Approach for Personalized Expert Recommendation (TAPER) Bet. Homo. Users Bet. Hetero. Bet. Hetero. Experts Bet. Hetero. Bet. Homo. minimize U (n),xx Topics Bet. Homo. Basic Tensor-based Recommendation n=1 Contextual Information bet. Homogeneous Entities 1 2 [[U (1),U (2),U (3) ]]k 2 X 3X F + ku (n) 2 X3X kx k k k 2 F + tr(z (n)t L n Z (n) ) 2 n=1 + 2 (ka U (1) U (2)T k 2 F + kb U (1) U (3)T k 2 F + kc U (2) U (3)T k 2 F ), Contextual Information bet. Heterogeneous Entities subject to X = T,U (n) = Z (n) 0,n=1, 2, 3 : control the weight of contextual information bet. homogeneous entities. : control the weight of contextual information bet. heterogeneous entities. Alternating Direction Method of Multipliers (ADMM) is applied. Recommendation are conducted based on the estimated tensor X.

14 State-of-the-art Methods Most Popular (MP) User-based Collaborative Filtering (UCF) Matrix Factorization (MF) Tensor Factorization (TF) Variants of Proposed TAPER Geo-based TAPER (G-TAPER) Topical-based TAPER (T-TAPER) Social-based TAPER (S-TAPER) Contextual Personalized Expert Recommendation (TAPER)

15 Experiments: Recommendation Effectiveness TF > MF TF > MF TF > MF TAPER TF MF G TAPER T TAPER S TAPER UCF MP Top 5 Top 10 Top 15 Tensor Factorization (TF) has a better performance than Matrix Factorization (MF)

16 Experiments: Recommendation Effectiveness TAPER TF MF G TAPER T TAPER S TAPER UCF MP Top 5 Top 10 Top 15 TAPER has the best performance comparing with other state-of-the-art methods

17 Experiments: Recommendation Effectiveness TAPER TF MF G TAPER T TAPER S TAPER UCF MP Top 5 Top 10 Top 15 Social ties of users and experts provide more significant contributions to the personalized expert recommendation

18 Experiments: Impact of Contextual Preferences Bet. Heterogeneous Entities Bet. Homogeneous Entities G TAPER T TAPER S TAPER TAPER Contextual preferences between homogeneous entities play a more important role than ones between heterogeneous entities

19 Experiments: Consistency TAPER TF MF Fraction of Training Data TAPER consistently outperforms both Matrix Factorization (MF) and Tensor Factorization (TF) in

20 Conclusions and Future Work The user of contextual information can lead to improve the accuracy of personalized expert recommendation based on a tensor-based approach. Social ties of users and experts provide more significant contributions to the personal expert recommendation than the geospatial and topical. Contextual preferences between homogeneous entities play a more important role than ones between heterogeneous entities. Future Work: Integrate additional contextual signals (e.g., temporal factors). Distributed TAPER for large-scale data.

21 Thank you! Q & A

22 Modeling Geo-Spatial Preferences Bet. Homogeneous Entities Bet. Heterogeneous Entities u i e j l(u i ) l(u j ) e i user i expert j location of user i num. of users selecting expert i in the location of user j across topics H G (u i,u j )=exp( Users Dist(u i,u j ) ) A G B G ti adjacency matrix of the spatial popularity of an expert in the location of a user. adjacency matrix of the spatial popularity of a topic in the location of a user. distribution of distances bet. users and experts in a topic i. F AG = ka G U (1) U (2)T k 2 F F BG = kb G U (1) U (3)T k 2 F Experts Topics V G (e i,e j )=exp( Dist(l(e i ),l(e j )) X u i 2U ( l(u i) e i l(u i ) e j ) 2 ) W G (t i,t j )=1 D KL ( ti k tj )

23 Modeling Topical Preferences Bet. Homogeneous Entities Bet. Heterogeneous Entities H T (u i,u j )= T T u i Tuj S T ui Tuj exp(x (o t u i o t u j )P t ) t2t T ui o t u i P t set of topics of a user is interested in num. of experts a user labeled in a topic t probability of being interested in a topic t Users T ei t e i set of topics of an expert has expertise num. of times an expert labeled by users in a topic t F BT = kb T U (1) U (3)T k 2 F Experts F CT = kc T U (2) U (3)T k 2 F Topics V T (e i,e j )= T T e i Tej S T ei Tej exp(x ( t t e i ej )P t ) t2t B T C T affinity matrix where an element indicates if a user is interested in certain topic affinity matrix where an element indicates the num. of times that an expert has been recognized by users in a topic

24 Modeling Social Preferences Bet. Homogeneous Entities Bet. Heterogeneous Entities H S (u i,u j )= F u i T Fuj F ui S Fuj Users F ei F ui set of users an expert follows set of users a user follows F AS = ka S U (1) U (2)T k 2 F A S adjacency matrix where the element indicates if a user follows an expert Experts Topics V S (e i,e j )= F e i T Fej F ei S Fej