TopicSensitive Page Rank Taher H Haveliwala Page Rank
- Slides: 44
Topic-Sensitive Page. Rank Taher H. Haveliwala
Page. Rank l Importance is propagated l A global ranking vector is pre-computed
Page. Rank
Topic-Sensitive Page. Rank l Basic ¡ For idea each topic, the importance scores for each page are computed ¡ Composite score of a page are calculated by combining the scores of the page based on the topics of the query
Topic-Sensitive Page. Rank l ODP-Biasing The top level categories of the Open Directory (16 topics) is used l Let Tj be the set of URLs in the ODP categories cj l In computing the Page. Rank vector for topic cj, we replace the uniform damping vector by the nonuniform vector where l l It will be referred as
Topic-Sensitive Page. Rank We chose to make P(cj) uniform
Topic-Sensitive Page. Rank
Experiment
Experimental Results l Similarity ¡ overlap Measure for Induced Rankings of two sets A and B l ¡ Kendall’s l = . k = 20 distance measure
Experimental Results
Experimental Results
Experimental Results
Experimental Results
Experimental Results
Experimental Results l Query-Sensitive ¡ User Scoring Study 10 queries (randomly selected from our test set) l 5 volunteers l For each query, the volunteer was shown 2 result rankings: l • 1. top 10 results ranked with the unbiased Page. Rank vector • 2. top 10 results ranked with the topic-sensitive Page. Rank vector
Experimental Results ¡ User l Study( con’t) The volunteer was asked to • 1. select all URLs which were “relevant” to the query • 2. select the ranking list which is better (They were not told anything about how either of the rankings was generated. )
Experimental Results
Experimental Results
Experimental Results l Context-Sensitive Scoring
Experimental Results
Other issues l Search Context ¡ hierarchical directory ¡ users’ browsing patterns ¡ Bookmarks ¡ email archives
Other issues ¡ Flexibility l Apply to any kinds of context ¡ Transparency l tune the classifier used on the search context, or adjust topic weights ¡ Privacy l a client-side program could use the user context to generate the user profile locally ¡ Efficiency l query-time cost and the offline preprocessing cost is low
Automatic Identification of User Interest For Personalized Search Feng Qiu Junghoo Cho
User Preference Representation l Topic ¡T Preference Vector = [T(1), …, T(m)] ¡ T(i) represents the user’s degree of interest in the ith topic ¡
User Preference Representation
User Model l Topic-Driven Random Surfer Model • The user browses the web in a two-step process. • First, the user chooses a topic of interest t for the ensuing sequence of random walks with probability T(t) • Then with equal probability, she jumps to one of the pages on topic t • Starting from this page, the user then performs a random walk, such that at each step, with probability d, she randomly follows an out-link on the current page; with the remaining probability 1 -d she gets bored and picks a new topic of interest for the next sequence of random walks based on T and jumps to a page on the chosen topic. • This process is repeated forever.
User Model l Topic-Driven Searcher Model • The user always visits web pages through a search engine in a two-step process. • First, the user chooses a topic of interest t with probability T(t). • Then the user goes to the search engine and issues a query on the chosen topic t. • The search engine then returns pages ranked by TSPRt(p), on which the user clicks.
User Model l Relationship between V and T ¡ Under Topic-Driven Random Surfer Model ¡ Under Topic-Driven Searcher Model
Learning Topic Preference Vector l Problem ¡ Given V and TSPRi, find T satisfies
Learning Topic Preference Vector l Linear regression ¡ Minimize l Maximum the square-root error likelihood estimator ** ¡ l = the probability that the user visits the page p
Ranking Search Results Using Topic Preference Vectors l Ranking l because l of page p =
Evaluation Metrics l Accuracy ¡ Te of topic preference vector is our estimation based on the user’s click history ¡ T is the user’s actual topic preference vector
Evaluation Metrics l Accuracy ¡ Kendall of personalized ranking distance between and ¡ is the sorted list of top-k pages based on the estimated personalized ranking scores ¡ is the sorted list of top-k pages computed the user ‘s true preference vector
Evaluation Metrics l Improvement ¡ Average in search quality rank of relevant pages in the search result ¡S denotes the set of the pages the user u selected ¡ R(p) is the ranking of the page p
Experiments l User Study ¡ 10 subjects in the UCLA Computer Science Department ¡ 04/2004 – 10/2004 (6 months) ¡ Queries to Google, results and clicked URLs average number of queries per subject = 255. 6 l average number of clicks per query = 0. 91 l
Experiments l Accuracy of Learning Method ¡ synthetic dataset generated by simulation based on our topic-driven searcher model l Generation of topic preference vector • Randomly choose K topics and assign random weight for them. The weight of others are set to zero. The vector is then normalized l Generation of click history • Use the generated topic preference vector to generate the clicks by the visit probability distribution dictated by the topic-driven searcher model
Experiments ¡ Accuracy of estimated topic preference vector
Experiments ¡ Accuracy of estimated topic preference vector
Experiments l Accuracy of Personalized Page. Rank
Experiments l Accuracy of Personalized Page. Rank
Experiments l Quality of Personalized Search
Experiments l Quality of Personalized Search
Conclusion l Proposed a framework to investigate the problem of personalizing web searching by the user search history and TSPR l Conducted both theoretical and real life experiments to evaluate the approach
l Thank you
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