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

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  • 11/23/12
  • 使用 folksonomt 可以以使用者的觀點作考量 , 相較於原定的分類也為節目帶來更多描述 Twofold: 系統可以獲得更多語意關聯 , 如此也可形成更具相關性的推薦 11/23/12
  • CBF 可以為使用者帶來不曾評價過的內容 , 為了實踐此部分 , 系統需要能夠將項目的特色對應到使用者檔案裡的相關特色 11/23/12
  • //User tag cloud 包含一些系統根據使用者初始喜好添加的 tag, 這些 tag 可能是使用者從未指派 User tag cloud 包含使用者從未指派給該項目的 tag 11/23/12
  • 11/23/12
  • Target-user cloud 會被用來比對 user’s tag cloud 作 item 的推薦 右邊的會根據使用者的初始喜好來加入系統座位初始化的 user tags cloud( 這是為了避免 cold-start) 11/23/12
  • 接著 juan 對這兩個節目評價 , 這些資訊可工稍候的 CF 利用 11/23/12
  • Item-base 而 user-base 是門檻直 7 的推薦 11/23/12
  • 這個節目類別分類是用來克服 cold-stat 問題 11/23/12
  • 11/23/12
  • Transcript

    • 1. This paper appears in:Internet Computing, IEEEDate of Publication: Nov.-Dec. 2010Product Type: Journals & MagazinesExploiting Social Tagging in a Web 2.0Recommender System Ana Belén Barragáns-Martínez Centro Universitario de la Defensa en la Escuela Naval Militar de Marín, Spain Marta Rey-López Consellería de Educación 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 system’s 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 SystemsThe standard CF approach presents some well-knownproblems:  gray-sheep problem  cold-start problem  first-rater problemThe primary drawback to CBF systems is theirtendency to overspecialize item selection .We adopt a hybrid approach(based on social tagging)for the TV recommender domain.
    • 6. Hybrid Recommender Systems
    • 7. Tag-Based Recommenders queveo.tv lets users give items tags to describe them.Use these tags to build both User and Item tagclouds. The weight of the tags is proportional to the number of times they have been assigned In user tag clouds, a tag’s weight is also proportionalto the ratings the users gave the items. 7
    • 8. User tag cloud In user tag clouds, a tag’s 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 assignedto it. Item tag clouds reflect the relationships between the system’s 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 tag’s weight is also proportional to the ratings the users gave the items.
    • 11. Social content-based recommendations The relationships between the user’s and item’s 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 - CBFA new user Juan is entering queveo.tv. He selects thecategories “Documentaries: General and Medical” and“Sports: Basketball” as his likes.  The CBF algorithm’s 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 - CFThe main goal of our item-based CF approach is toprecisely fill these empty values with predictions.For a recommendation threshold of seven, the CFalgorithm recommended the documentary and the TVseries. CF(item-based) recommendation 14
    • 15. Illustrative Example - CFMore Than a Game was rated with the same pattern asThe Operation: Surgery Live — that is, both Marta andFernando gave a similar rating to both. 15
    • 16. Illustrative Example - CFEnrique and Fernando gave similar ratings to NBA Actionand House 16
    • 17. Illustrative Example - CFWithout the tag-based recommender, the final resultswould be The Operation: Surgery Live (both CF and CBFrecommend it) and House. CF recommendation 17
    • 18. Illustrative Example - SCBFWe compose the provisional user tag cloud, which thesystem uses until users have their own.The system can now use the information from both Juan’stag cloud and the tag clouds from each program. 18
    • 19. Illustrative Example - SCBFJuan has used terms such as surgery and doctor to tag thedocumentary The Operation: Surgery Live.The SCBF algorithm finds new relevant content torecommend: the TV series Nip/Tuck (focused on a plasticsurgery practice). 19
    • 20. Illustrative Example - SCFthe SCF algorithm also recommends to Juan the movieApollo 13.Because its target-user tag cloud contains tags that arealso in Juan’s tag cloud or related to them through thefolksonomy. 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 .

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