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Exploiting social tagging in a web 2.0 recommender system(lab)

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This paper proposes using social tagging information from a recommender system to improve recommendations. It introduces two new tag-based recommendation techniques: social content-based filtering (SCBF) and social collaborative filtering (SCF). SCBF compares a user's tag cloud to item tag clouds, while SCF builds a target user tag cloud and compares it to other users' clouds. The techniques help address issues with standard collaborative filtering like cold start and provide more coverage. An example shows how SCBF could recommend a TV series based on a user's interest in surgery tags. The paper concludes the techniques provide more semantic connections through folksonomies and coverage.

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This paper appears in: Internet Computing, IEEE Date of Publication: Nov.-Dec. 2010 Product Type: Journals & Magazines Exploiting Social Tagging in a Web 2.0 Recommender System Ana Beln Barragns-Martnez Centro Universitario de la Defensa en la Escuela Naval Militar de Marn, Spain Marta Rey-Lpez Consellera de Educacin e O.U., Spain Enrique Costa-Montenegro, Fernando A. Mikic-Fonte, Juan C. Burguillo, and Ana Peleteiro University of Vigo, Spain Student: Chen-Ting Huang Advisor: Yin-Fu Huang
Issues To take advantage of Web 2.0 applications, the authors propose using information obtained from social tagging to improve recommendations. The Web 2.0 TV program recommender queveo.tv currently combines content-based and collaborative filtering techniques. This article presents a novel tag-based recommender to enhance the recommending engine by improving the coverage and diversity of the suggestions.
Motivations The ratings are related to the tags rather than to the items themselves, which makes them valuable even when the items are no longer in the system. The hybrid proposal works well because the algorithms complement each other; CBF(content-based filtering) and CF(collaborative filtering) recommends. This lets us enrich our recommender system with two new recommendation techniques, SCF and SCBF, both based on social tagging information.
Social tagging and Folksonomy we propose taking advantage of the systems social tagging capabilities to enrich the quality of the recommendations. Social tagging also lets us create a folksonomy, which shows the relationships between the different tags. Such an approach would improve the quality of recommendations twofold.
Recommender Systems The standard CF approach presents some well-known problems: ? gray-sheep problem ? cold-start problem ? first-rater problem The primary drawback to CBF systems is their tendency to overspecialize item selection . We adopt a hybrid approach(based on social tagging) for the TV recommender domain.
Hybrid Recommender Systems
Tag-Based Recommenders queveo.tv lets users give items tags to describe them. Use these tags to build both User and Item tag clouds. The weight of the tags is proportional to the number of times they have been assigned In user tag clouds, a tags weight is also proportional to the ratings the users gave the items. 7
User tag cloud In user tag clouds, a tags weight is also proportional to the ratings the users gave the items. User clouds consist of tags users have never assigned. User tag cloud 8
Item tag cloud Item tag cloud includes the tags users have assigned to it. Item tag clouds reflect the relationships between the systems tags. We represent this structure, called a folksonomy. Item tag cloud 9
Social content-based recommendations The simplest way of recommending items to users is by directly comparing their tag clouds. We measure the number of coincident tags of both tag clouds (direct relationship, R0) ?The weight of the tags is proportional to the number of times they have been assigned ?In user tag clouds, a tags weight is also proportional to the ratings the users gave the items.
Social content-based recommendations The relationships between the users and items tags (one-hop relationship, R1)
Social collaborative recommendations A new tag cloud for the items, called the target-user tag cloud The system compares it with the potential users tag clouds to obtain their similarity
Illustrative Example - CBF A new user Juan is entering queveo.tv. He selects the categories Documentaries: General and Medical and Sports: Basketball as his likes. ? The CBF algorithms output consists of those TV programs that match his likes More Than a Game and NBA Action ? Recommends another documentary, The Operation: Surgery Live. CBF recommendation
Illustrative Example - CF The main goal of our item-based CF approach is to precisely fill these empty values with predictions. For a recommendation threshold of seven, the CF algorithm recommended the documentary and the TV series. CF(item-based) recommendation 14
Illustrative Example - CF More Than a Game was rated with the same pattern as The Operation: Surgery Live that is, both Marta and Fernando gave a similar rating to both. 15
Illustrative Example - CF Enrique and Fernando gave similar ratings to NBA Action and House 16
Illustrative Example - CF Without the tag-based recommender, the final results would be The Operation: Surgery Live (both CF and CBF recommend it) and House. CF recommendation 17
Illustrative Example - SCBF We compose the provisional user tag cloud, which the system uses until users have their own. The system can now use the information from both Juans tag cloud and the tag clouds from each program. 18
Illustrative Example - SCBF Juan has used terms such as surgery and doctor to tag the documentary The Operation: Surgery Live. The SCBF algorithm finds new relevant content to recommend: the TV series Nip/Tuck (focused on a plastic surgery practice). 19
Illustrative Example - SCF the SCF algorithm also recommends to Juan the movie Apollo 13. Because its target-user tag cloud contains tags that are also in Juans tag cloud or related to them through the folksonomy. By SCF By SCBF
Conclusions and Future Work Using tag-based recommendation techniques lets queveo.tv gain more semantic interconnections thanks to the use of folksonomies. They also obtain greater coverage because additional relevant items are now included among the recommendations . In the future, we will study the possibility of reducing the weights of the tags as they get older .

More Related Content

Exploiting social tagging in a web 2.0 recommender system(lab)

  • 1. This paper appears in: Internet Computing, IEEE Date of Publication: Nov.-Dec. 2010 Product Type: Journals & Magazines Exploiting Social Tagging in a Web 2.0 Recommender System Ana Beln Barragns-Martnez Centro Universitario de la Defensa en la Escuela Naval Militar de Marn, Spain Marta Rey-Lpez Consellera de Educacin e O.U., Spain Enrique Costa-Montenegro, Fernando A. Mikic-Fonte, Juan C. Burguillo, and Ana Peleteiro University of Vigo, Spain Student: Chen-Ting Huang Advisor: Yin-Fu Huang
  • 2. Issues To take advantage of Web 2.0 applications, the authors propose using information obtained from social tagging to improve recommendations. The Web 2.0 TV program recommender queveo.tv currently combines content-based and collaborative filtering techniques. This article presents a novel tag-based recommender to enhance the recommending engine by improving the coverage and diversity of the suggestions.
  • 3. Motivations The ratings are related to the tags rather than to the items themselves, which makes them valuable even when the items are no longer in the system. The hybrid proposal works well because the algorithms complement each other; CBF(content-based filtering) and CF(collaborative filtering) recommends. This lets us enrich our recommender system with two new recommendation techniques, SCF and SCBF, both based on social tagging information.
  • 4. Social tagging and Folksonomy we propose taking advantage of the systems social tagging capabilities to enrich the quality of the recommendations. Social tagging also lets us create a folksonomy, which shows the relationships between the different tags. Such an approach would improve the quality of recommendations twofold.
  • 5. Recommender Systems The standard CF approach presents some well-known problems: ? gray-sheep problem ? cold-start problem ? first-rater problem The primary drawback to CBF systems is their tendency to overspecialize item selection . We adopt a hybrid approach(based on social tagging) for the TV recommender domain.
  • 7. Tag-Based Recommenders queveo.tv lets users give items tags to describe them. Use these tags to build both User and Item tag clouds. The weight of the tags is proportional to the number of times they have been assigned In user tag clouds, a tags weight is also proportional to the ratings the users gave the items. 7
  • 8. User tag cloud In user tag clouds, a tags weight is also proportional to the ratings the users gave the items. User clouds consist of tags users have never assigned. User tag cloud 8
  • 9. Item tag cloud Item tag cloud includes the tags users have assigned to it. Item tag clouds reflect the relationships between the systems tags. We represent this structure, called a folksonomy. Item tag cloud 9
  • 10. Social content-based recommendations The simplest way of recommending items to users is by directly comparing their tag clouds. We measure the number of coincident tags of both tag clouds (direct relationship, R0) ?The weight of the tags is proportional to the number of times they have been assigned ?In user tag clouds, a tags weight is also proportional to the ratings the users gave the items.
  • 11. Social content-based recommendations The relationships between the users and items tags (one-hop relationship, R1)
  • 12. Social collaborative recommendations A new tag cloud for the items, called the target-user tag cloud The system compares it with the potential users tag clouds to obtain their similarity
  • 13. Illustrative Example - CBF A new user Juan is entering queveo.tv. He selects the categories Documentaries: General and Medical and Sports: Basketball as his likes. ? The CBF algorithms output consists of those TV programs that match his likes More Than a Game and NBA Action ? Recommends another documentary, The Operation: Surgery Live. CBF recommendation
  • 14. Illustrative Example - CF The main goal of our item-based CF approach is to precisely fill these empty values with predictions. For a recommendation threshold of seven, the CF algorithm recommended the documentary and the TV series. CF(item-based) recommendation 14
  • 15. Illustrative Example - CF More Than a Game was rated with the same pattern as The Operation: Surgery Live that is, both Marta and Fernando gave a similar rating to both. 15
  • 16. Illustrative Example - CF Enrique and Fernando gave similar ratings to NBA Action and House 16
  • 17. Illustrative Example - CF Without the tag-based recommender, the final results would be The Operation: Surgery Live (both CF and CBF recommend it) and House. CF recommendation 17
  • 18. Illustrative Example - SCBF We compose the provisional user tag cloud, which the system uses until users have their own. The system can now use the information from both Juans tag cloud and the tag clouds from each program. 18
  • 19. Illustrative Example - SCBF Juan has used terms such as surgery and doctor to tag the documentary The Operation: Surgery Live. The SCBF algorithm finds new relevant content to recommend: the TV series Nip/Tuck (focused on a plastic surgery practice). 19
  • 20. Illustrative Example - SCF the SCF algorithm also recommends to Juan the movie Apollo 13. Because its target-user tag cloud contains tags that are also in Juans tag cloud or related to them through the folksonomy. By SCF By SCBF
  • 21. Conclusions and Future Work Using tag-based recommendation techniques lets queveo.tv gain more semantic interconnections thanks to the use of folksonomies. They also obtain greater coverage because additional relevant items are now included among the recommendations . In the future, we will study the possibility of reducing the weights of the tags as they get older .

Editor's Notes

  • #3: 11/23/12
  • #5: ʹ folksonomt ʹߵ^c , ^ԭķҲ鹝Ŀ Twofold: ϵyԫ@øZP“ , ҲγɸPԵ] 11/23/12
  • #6: CBF Ԟʹߎur^ă , ˌ`˲ , ϵyҪ܉ĿɫʹߙnePɫ 11/23/12
  • #9: //User tag cloud һЩϵyʹ߳ʼϲӵ tag, @Щ tag ʹߏδָ User tag cloud ʹߏδָɽoԓĿ tag 11/23/12
  • #11: 11/23/12
  • #13: Target-user cloud ÁȌ users tag cloud item ] ߅ĕʹߵijʼϲÁϵyλʼ user tags cloud( @Ǟ˱ cold-start) 11/23/12
  • #14: juan @ɂĿur , @ЩYӍɹԺ CF 11/23/12
  • #16: Item-base user-base Tֱ 7 ] 11/23/12
  • #19: @ĿeÁ˷ cold-stat } 11/23/12
  • #22: 11/23/12