Query Chains: Learning to Rank from Implicit Feedback

This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.Comment: 10 page

Identification of User Search Targets Using Feed Backs 1

Abstract Different users may have different search objectives and goals for a huge and confusing search item. The search engine performance can be improved by identifying and analyzing the search goals . In this paper, we propose a studied the approach to identify the user search goals by analyzing search engine query logs. The search goals of different users by clustering the proposed feedback from the search sessions.. to get the best results it is necessary to capture different user search goals. These user goals are nothing but information on different aspects of a query that different users want to obtain. The judgment and analysis of user search goals can be improved by the relevant result obtained from search engine and user's feedback. Here, feedback sessions are used to discover different user search goals based on series of both clicked and un clicked URL's. The pseudo-documents are generated to better represent feedback sessions which can reflect the information need of user. With this the original search results are restructured and to evaluate the performance of restructured search results, classified average precision is used. Keywords Search Goals, Feedback Sessions, Pseudo-Documents I. Introduction Web mining is one of the applications of data mining techniques to discover knowledge from the web. In web search, users are submitted queries to the search engines to get relevant information. But many search engines results are not informative and failed to produce results according to the user search goals. Users are usually giving some vague keywords representing their interests in their minds. Such keywords do not match with the results produced by the search engines. Many works about user search goals analysis should be carried out. Some users give ambiguous queries to the search engines they get mostly the irrelevant results. User search goals are classified as Navigational and Informational, the queries that seek a single website or webpage and queries that reflect the intent of the user to perform a particular transaction respectively. Many related works have been carried out according to the web search applications and the user search goals. In previous works, clustering is done on a set of top ranked results. The user search logs information is not analyzed and the feedback sessions are not considered. Analyzing the clicked URLS only from the web search logs. They only identify whether a pair of queries belong to the same goal or mission and does not care about what the goal is in detail. Semantic based web search for a particular query and the similarity between the words are carried out. Various algorithms such as star clustering algorithm, k-means clustering algorithm are used for clustering the pseudo documents but it also does not cluster the relevant information according to the user search goals. In clustering the cluster labels discovered are also not informative. User search goal is the information on different aspects of a query that users wants to obtain. Information need is a user's desire to obtain the relevant information to satisfy his need. To cluster web search results, the URLs are analyzed by extracting the titles and snippets. But all those works produced noisy results and does not obtain the user search goals precisely. When more irrelevant and relevant results are produced by the search engines it is tim

Context Models For Web Search Personalization

We present our solution to the Yandex Personalized Web Search Challenge. The aim of this challenge was to use the historical search logs to personalize top-N document rankings for a set of test users. We used over 100 features extracted from user- and query-depended contexts to train neural net and tree-based learning-to-rank and regression models. Our final submission, which was a blend of several different models, achieved an NDCG@10 of 0.80476 and placed 4'th amongst the 194 teams winning 3'rd prize