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Trust-aware Recommender Systems

Trust-aware Recommender Systems. Massa, P. & Avesani , P. Recommender System 2007 Presented by Danielle Lee. Problem & Purpose. Poor performance of collaborative filtering due to Data sparsity Ad hoc user profiles / Copy profile attack

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Trust-aware Recommender Systems

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  1. Trust-aware Recommender Systems Massa, P. & Avesani, P. Recommender System 2007 Presented by Danielle Lee

  2. Problem & Purpose • Poor performance of collaborative filtering due to • Datasparsity • Ad hoc user profiles / Copy profile attack • Cold start users & Newly added items (provided few ratings) • To search trustable users by exploiting trust propagation over the trust network, not to search similar users as CF • Just providing a trust statement is effective way of bootstrapping RSs for new users with very few ratings.

  3. Trust Networks and Trust Metrics • Trust Metrics : Algorithms whose goal is to predict, based on the trust network, the trustworthiness of “unknown” users. • Local Trust Metrics : the very personal and subjective views of the users. Different value of trust in other users for every user MoleTrust • Global Trust Metrics : a global “reputation” value that approximates how the community as a whole considers a certain user.  PageRank

  4. Trust-Aware Recommender Architecture

  5. Data Set • Epinions.com which a consumers opinion site • Review and rate items (such cars, books, movies, softwares, etc.) • Express Web of Trust • Inserting a user in the Web of Trust equals to a trust statement of value “1.” • 49,290 users, 139,738 different items, 664,824 reviews and 487,181 trust statement. • 52.84% cold start users having less than 55 reviews • Mean number of users in the Web of Trust is 9.88 (std. dev 32.85) • Compared with Movielens, Epinions have much more coldstart users and high sparsity in data.

  6. Evaluation Measures (1) • Mean Absolute Error (MAE) • Mean Absolute User Error (MAUE) • MAE for every user is computed independently • Average all the MAEs. • Ratings Coverage • The fraction of ratings for which the RS algorithm is able to produce a predicted rating. • User Coverage • The portion of users for which the RS is able to predict at least one rating.

  7. Evaluation Measures (2) • Cold start users : provided 1 ~ 4 ratings • Heavy users : provided more than 10 ratings • Opinionated users : Provided more than 4 ratings and the std. dev. is greater than 1.5 • Black sheep : provided more than 4 ratings and for which the average distance of their rating on item i with respect to mean rating of item i is greater than 1 • Niche item : received less than 5 ratings • Controversial items : received rating whose std. dev. Is greater than 1.5

  8. Input_Results (Simple Algorithm) • To explore which MAE a simple algorithm would achieve • Always5:returns always 5 as the predicted rating a user would give to an item • MAE : 1.008. • Average rating : returns the mean of the ratings provided by one users • MAE : 0.9243 • The most of the rating in the data set is in fact 5, and on the controversial items, these performs very badly. • MAE value over all rating is not a useful way to compare different algorithms.

  9. Input_Results (TrustAll) • TrustAll : predicts a rating for a certain item the unweighted average of all the ratings given to that item by all the users but the active users. • TrustAll (0.821) outperformed standard CF (0.843) in MAE and TrustAll (88.20%) more predictable than CF (51.28%) . • On cold start user, TrustAll (0.856) outperformed CF (1.094) in MAE and TrustAll (92.92%) is more predictable than CF (3.22%) • On controversial items, CF (1.515) outperformed TrustAll (1.741) • It is due to the sparsity of data and relative low variance in rating values.

  10. Input_Results (Overall) • MT1 : The users explicitly trusted by users ont propagating trust. • MT1 is able to predict fewer ratings than CF but the predictions are spread more equallly over all the users and MT1’s prediction is more accurate than CF. • Especially MT1 works better for cold start users. • MT2, MT3 & MT4 : Calculating trust propagation metrics using distance 2, 3 & 4 respectively. • Average number of directly trusted users (MT1) : 9.88 • Propagated number of users at distance 2 (MT2) : 399.89 • 4,386.32 for MT3 and 16,333.94 for MT4 • The larger the trust propagation horizon, the greater the coverage, but the errors can increase as well.

  11. Input_Results (Overall)

  12. Input_Results (Overall)

  13. Output_Results • The results of “rating predictor” • For the combined data of CF + MTx (x is from 1 to 4), the coverage is greater than the coverage of the two techniques but the error is between CF and MTx (worse than MTx, better than CF)

  14. Conclusion & Discussion • Ratings of directly trusted users achieves the smallest error with an acceptable coverage, particularly for controversial item and black sheep. • For cold start users, CF totally failed and trusted users achieves very small error and good coverage. • According to the different characteristic of the data set, the algorithms work differently.

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