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Optimizing search engines using clickthrough data ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ ΣΧΟΛΗ ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΜΗΧΑΝΙΚΩΝ ΥΠΟΛΟΓΙΣΤΩΝ ΤΟΜΕΑΣ ΤΕΧΝΟΛΟΓΙΑΣ.

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Presentation on theme: "Optimizing search engines using clickthrough data ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ ΣΧΟΛΗ ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΜΗΧΑΝΙΚΩΝ ΥΠΟΛΟΓΙΣΤΩΝ ΤΟΜΕΑΣ ΤΕΧΝΟΛΟΓΙΑΣ."— Presentation transcript:

1 Optimizing search engines using clickthrough data ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ ΣΧΟΛΗ ΗΛΕΚΤΡΟΛΟΓΩΝ ΜΗΧΑΝΙΚΩΝ ΚΑΙ ΜΗΧΑΝΙΚΩΝ ΥΠΟΛΟΓΙΣΤΩΝ ΤΟΜΕΑΣ ΤΕΧΝΟΛΟΓΙΑΣ ΠΛΗΡΟΦΟΡΙΚΗΣ ΚΑΙ ΥΠΟΛΟΓΙΣΤΩΝ

2 2 Problem Optimization of web search results ranking  Ranking algorithms mainly based on similarity Similarity between query keywords and page text keywords Similarity between pages (Page Rank)  No consideration for user personal preferences Digital libraries High rank Vacation Low rank

3 3 Problem E.g.: query “delos”  Digital libraries  Archaeology  Vacation Room for improvement by incorporating user behaviour data: user feedback  Use of previous implicit search information to enhance result ranking Vacation Low rank Digital libraries High rank

4 4 Types of user feedback  Explicit feedback User explicitly judges relevance of results with the query  Costly in time and resources  Costly for user  limited effectiveness  Direct evaluation of results  Implicit feedback Extracted from log files  Large amount of information  Indirect evaluation of results through click behaviour

5 5 Implicit feedback (Categories) Clicked results  Absolute relevance: Clicked result -> Relevant  Risky: poor user behaviour quality Percentage of result clicks for a query Frequently clicked groups of results Links followed from result page  Relative relevance: Clicked result -> More relevant than non-clicked  More reliable Time  Between clicks E.g., fast clicks  maybe bad results  Spent on a result page E.g., a lot of time  relevant page  First click, scroll E.g., maybe confusing results

6 6 Implicit feedback (Categories) Query chains: sequences of reformulated queries to improve results of initial search  Detection: Query similarity Result sets similarity Time  Connection between results of different queries: Enhancement of a bad query with another from the query chain Comparison of result relevance between different query results Scrolling  Time (quality of results)  Scrolling behaviour (quality of results)  Percentage of page (results viewed) Other features  Save, print, copy etc of result page  maybe relevant  Exit type (e.g. close window  poor results)

7 7 Joachims approach Clickthrough data  Relative relevance  Indicated by user behaviour study Method  Training of svm functions  Training input: inequations of query result rankings  Trained function: weight vector for features examined  Use of trained vector to give weights to examined features Experiments  Comparison of method with existing search engines

8 8 Clickthrough data Form  Triplets (q,r,c) = (query,ranked results,links clicked)  …in the log file of a proxy server Relative relevance (regarding a specific query)  d k more relevant than d l if d k clicked and d l not clicked and d k lower initial ranking than d l d5 more relevant than d4 d5 more relevant than d2 d3 more relevant than d2

9 9 Clickthrough data (Studies) Why not absolute relevance?  User behaviour influenced by initial ranking  Study: Rank and viewership Percentage of queries where a user viewed the search result presented at a particular rank  Conclusion: Most times users view only the few first results  Study: Rank and viewership Percentage of queries where a user clicked the result presented at a particular rank, both in normal and swapped conditions  Conclusion: Users tend to click on higher ranked results irrespective of content

10 10 Method (System train input) Data extracted from log  Relevance inequalities: d k < r q d l d k more relevant than d l for query q  Construct relevance inequalites for every query q and every result pair (d k, d l ) for q For each link di (result of query q):  construct a feature vector Φ(q,d i ) d5 more relevant than d4 d5 more relevant than d2 d3 more relevant than d2

11 11 Method (System train input) Feature vector:  Describes the quality of the match between a document d i and a query q  Φ(q,d i ) = [rank_X, top1_X, top10_X, …..]

12 12 Method (System train input) Weight vector:  w = [w1, w2,…,wn]  Assigns weights to each of the features in Φ(q,d i )  Sd i = w * Φ(q,d i ) assigns a score to document di for query q  w: unknown  must be trained by solving the system of relative relevance inequalities derived from clickthrough data

13 13 Method (System training) Initial input transformation:  d k Sd k > r m Sd l =>  w * Φ(q m,d k ) > w * Φ(q m,d l ) =>  [w1, w2,…,wn] Φ(q m,d k ) > [w1, w2,…,wn] Φ(q m,d l )  System of relevance inequalites for every query q and every result pair (d k, d l ) for q Object of training:  Knowing, for every clicked link d i, its feature vector,  Find an optimal weight vector w which satisfies most of the inequalities above (optimal solution)  With vector w known, we can calculate a score for every link, whether clicked or not, hence a new ranking d5 more relevant than d4 d5 more relevant than d2 d3 more relevant than d2

14 14 Method (System training) Intuitively  Example: 2 dimension feature vector X-axis: similarity between query terms and link text Y-axis: time spent on link page  Looking for optimal weight vector w  If training input: r1 < r2 < r3 < r4 Optimal solution w1: text similarity more important for ranking  If training input: r2 < r3 < r1 < r4 Optimal solution w2: time more important for ranking  w optimized by maximizing δ Link 1 Link 2 Link 3 Link 4

15 15 Experiments Based on a meta-search engine  Combination of results from different search engines  “Fair” presentation of results: For a meta-meta-search engine combining 2 engines: Top z results of meta-search contain x results from engine A y results from engine b x + y = z |x – y| <= 1

16 16 Experiments Meta-search engine Assumptions  Users click more often on more relevant links  Preference for one of the search engines not influenced by ranking

17 17 Experiments (Results) Conditions  20 users (university students)  260 training queries collected  20 days of training  10 days of evaluation Comparison with  Google  MSNSearch  Toprank (meta-search for Google, MSNSearch, Altavista, Excite, Hotbot) Results

18 18 Experiments (Results) Weight vector

19 19 Open issues Trade-off between amount of training data and homogeneity Clustering algorithms to find homogenous groups of users Adaptation to the properties of a particular document collection Incremental online learning/feedback algorithm Protection from spamming

20 20 Joachims approach II Clickthrough data  Addition of new relative relevance criterions  Addition of query chains Method  Modified feature vector Rank features Term/document features  Constraints to the svm optimization problem To avoid trivial/incorrect solutions

21 21 Query chains Sequences of reformulated queries  Poor results for first query Too many/few terms Not representative terms  q1: “special collections”  q2: “rare books” Incorrect spelling  q1: “Lexis Nexus”  q2: “Lexis Nexis”  Execution of new query Addition/deletion of query terms to get better results  In every query of the sequence, searching for the same thing Ways of detection  Term similarity between queries  Percentage of common results  Time between queries

22 22 Query chains detection method 1 st strategy  Data recorded from log for 1285 queries: query, date, IP address, results returned, number of clicks on the results, session id uniquely assigned  Manual grouping of queries in query chains  Division of data set into training and testing set  Training Feature vector for every pair of query Training of a weight vector indicating which features are more important for a query pair to be a query chain  Testing Recognition of query chains in testing set Comparison with manual grouping 94.3% accuracy 2 nd strategy  Assumption: Query chain if: Queries from the same IP Time between 2 queries < 30 min 91.6% accuracy  Adoption of (simpler) 2 nd strategy

23 23 Clickthrough data (studies) Conclusion: Most times users view the first 2 results Conclusion: Most times users view the next result after the last clicked

24 24 Relevance criteria (query) Click > q Skip Above  d k more relevant than d l if d k clicked and d l not clicked and d k lower initial ranking than d l Click First > q No-Click Second  d 1 more relevant than d 2 if d 1 clicked d 2 not clicked d5 more relevant than d4 d5 more relevant than d2 d3 more relevant than d2 d1 more relevant than d2

25 25 Relevance criteria (query chain) Click > q1 Skip Above  d k more relevant than d l if d k clicked and d l not clicked and d k lower initial ranking than d l Click First > q1 No-Click Second  d 1 more relevant than d 2 if d 1 clicked d 2 not clicked FOR QUERY 1 (q’) d8 more relevant than d7 d8 more relevant than d6 FOR QUERY 1 (q’) d5 more relevant than d6 QUERY 1QUERY 2

26 26 Relevance criteria (query chain II) Click > q1 Skip Earlier Query Click First > q1 Top 2 Earlier Query FOR QUERY 1 (q’) d6 more relevant than d1 d6 more relevant than d2 d6 more relevant than d4 FOR QUERY 1 (q’) d6 more relevant than d1 d6 more relevant than d2 QUERY 1QUERY 2

27 27 Relevance criteria (Experiment) Sequences of reformulated queries  Search on 16 subjects  Relevance inequalities produced by previous criteria  Comparison with explicit relevance judgements on query chains

28 28 Method (SVM training) Feature vector consists of  Rank features φ fi rank (d, q)  Term/document features φ terms (d, q) Rank features  One φ fi rank (d, q) for every retrieval function fi examined  Every φ fi rank (d, q) consists of 28 features For ranks 1,2,3,…10,15,20,…100 in the initial ranking If rank of d in initial ranking ≤ 1,2,3,…10,15,20,…100 set 1 else set 0  Allow us to learn weights for and combine different retrieval functions

29 29 Method (SVM training) Term/document features  φ terms (d, q) consists of N*M features N number of terms M number of documents  Set 1 if di = d and tj ε q  Trains weight vector to learn assosiations between terms and documents

30 30 Method (Constraints) Problem  Most relevance criteria suggest that a lower ranked link (in the initial ranking) is better than a higher ranked link  Trivial solution: reverse the initial ranking by assigning negative values to weight vector Solution  Each w*φ fi rank (d, q) ≥ a minimun positive value

31 31 Experiments Based on a meta-search engine  Initial retrieval function from Nutch Training  9949 queries  7429 clicks  Pqc: Total of relative relevance inequalities  Pnc: A set of Pqc generated without use of query chains Results: (rel 0 initial ranking)

32 32 Experiments Results  Trained weights for term/document features

33 33 Open issues Tolerance of noise and malicious clicks Different weights in each query of a query chain Personalized ranking functions Improvement of performance

34 34 Related work (Fox et al. 2005) Evaluation of implicit measures Use of explicit ratings of user satisfaction Method  Bayesian modeling and decision trees  Gene analysis Results: Importance of combination of Clickthrough Time Exit type Features used:

35 35 Related work ( Agichtein, Brill, Dumais 2006 ) Incorporation of implicit feedback features directly into the initial ranking algorithm  BM25F  RankNet Features used:

36 36 Related work Query/links clustering based on features extracted from implicit feedback Score based on interrelations between queries and web pages

37 37 Possible extensions Utilization of time feedback  As relative relevance criterion  As a feature Other types of feedback  Scrolling  Exit type Combination with absolute relevance clickthrough feedback  Percentage of result clicks for a query  Links followed from result page Query chains  Improvement of detection method Link association rules  For frequently clicked groups of results Query/links clustering Constant training of ranking functions

38 38 References Search Engines that Learn from Implicit Feedback  Joachims, Radlinski Optimizing Search Engines Using Clickthrough Data  Joachims Query Chains: Learning to Rank from Implicit Feedback  Radlinski, Joachims Evaluating implicit measures to improve web search  Fox et al. Implicit Feedback for Inferring User Preference A Bibliography  Kelly, Teevan Improving Web Search Ranking by Incorporating User Behavior  Agichtein, Brill, Dumais Optimizing web search using web click-through data  Xue, Zeng, Chen, Yu, Ma, Xi, Fan

39 39 Clickthrough data (Studies) Why not absolute relevance?  Study: Rank and viewership Percentage of queries where a user clicked the result presented at a particular rank, both in normal and swapped conditions  Conclusion: Users tend to click on higher ranked results irrespective of content

40 40 Types of user feedback  Explicit feedback User explicitly judges relevance of results with the query  Costly in time and resources  Costly for user  limited effectiveness  Direct evaluation of results  Implicit feedback Extracted from log files  Large amount of information  Real user behaviour (not expert judgement)  Indirect evaluation of results through click behaviour


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