Web Search – Summer Term 2006 VI. Web Search - Ranking (c) Wolfgang Hürst, Albert-Ludwigs-University

Personalized and Topic-Sensitive PageRank Uniform distribution (1-d) to model jumps to random web pages is not realistic (bookmarks, known URLs,...) Idea : Use jump probability for personalization, i.e. - Instead of identity matrix: Weighting based on personal preferences (e.g. bookmarks) - Problem: Pre-calculation impossible (personal data!) and online calculation to expensive Another characteristic of PageRank: query independent - Might be critical if page with high PageRank accidentally gets selected as relevant - Idea: Create a topic-sensitive PageRank

Topic-Sensitive PageRank Basic idea : 1. Identify topic that might be interesting for the user (e.g. via classification of the query, eval. of context,...) 2. Use pre-calculated, topic-sensitive PageRank Similarities to personalization but - Fixed, pre-specified topics (can be pre-calculated!) - Depending on the actual situation (more flexible) Topic specific PageRank rank jd : Normally: Identity matrix (a ij = 1 or 1/N) Now: Topics c 1,..., c n, e.g. the 16 top-level categories from the Open directory project Topic dependent weighting (1/|T i |) Advantage: Can be calculated in advance SOURCE: [5]

Topic-Sensitive PageRank (cont.) Question : Which one to select during run time? Idea : Automatic classification of the topic based on the query q given by the user Extension: Consider context q' of query q, e.g. - surrounding text if query was entered via highlighting - based on the history (if available) etc. Calculation (e.g.) using a unigram language model: SOURCE: [5]

Topic-Sensitive PageRank (cont.) Alternative approach : Use probabilities, i.e. - Weighted summation of all topic specific PageRanks for one document - Weights: Depending on the probability of a particular topic being relevant given the query q - Definition: Query-Sensitive Importance Score s qd In practice: Usually just take the three topic-sensitive PageRanks with highest probability Disadvantages: - Fixed set of topics - Depends on training set SOURCE: [5]

References - Indexing [1] A. ARASU, J. CHO, H. GARCIA-MOLINA, A. PAEPCKE, S. RAGHAVAN: "SEARCHING THE WEB", ACM TRANSACTIONS ON INTERNET TECHNOLOGY, VOL 1/1, AUG Chapter 5 (Ranking and Link Analysis) [2] S. BRIN, L. PAGE: "THE ANATOMY OF A LARGE-SCALE HYPERTEXTUAL WEB SEARCH ENGINE", WWW 1998 Chapter 2 and [3] BORDER, KUMAR, MAGHOUL, RAGHAVAN, RAJAGOPALAN, STATA, TOMKINS, WIENER: "GRAPH STRUCTURE IN THE WEB", WWW 2000 [4] PAGE, BRIN, MOTWANI, WINOGRAD: "THE PAGERANK CITATION RANKING: BRINGING ORDER TO THE WEB", STANFORD TECHNICAL REPORT [5] HAVELIWALA: "TOPIC-SENSITIVE PAGERANK", WWW 2002

General Web Search Engine Architecture CLIENT QUERY ENGINE RANKING CRAWL CONTROL CRAWLER(S) USAGE FEEDBACK RESULTS QUERIES WWW COLLECTION ANALYSIS MOD. INDEXER MODULE PAGE REPOSITORY INDEXES STRUCTUREUTILITYTEXT (CF. [1] FIG. 1)