Incorporating Non-sequential interactions into Click Models
نویسندگان
چکیده
Click-through information is considered as a valuable source of users’ implicit relevance feedback. As user behavior is usually influenced by a number of factors such as position, presentation style and site reputation, researchers have proposed a variety of assumptions (i.e. click models) to generate a reasonable estimation of result relevance. The construction of click models usually follow some hypotheses. For example, most existing click models follow the sequential examination hypothesis in which users examine results from top to bottom in a linear fashion. While these click models have been successful, many recent studies showed that there is a large proportion of non-sequential browsing (both examination and click) behaviors in Web search, which the previous models fail to cope with. In this paper, we investigate the problem of properly incorporating non-sequential behavior into click models. We firstly carry out a laboratory eye-tracking study to analyze user’s non-sequential examination behavior and then propose a novel click model named Partially Sequential Click Model (PSCM) that captures the practical behavior of users. We compare PSCM with a number of existing click models using two real-world search engine logs. Experimental results show that PSCM outperforms other click models in terms of both predicting click behavior (perplexity) and estimating result relevance (NDCG and user preference test).
منابع مشابه
Interactions of non-polar and "Click-able" nucleotides in the confines of a DNA polymerase active site.
Modified nucleotides play a paramount role in many cutting-edge biomolecular techniques. The present structural study highlights the plasticity and flexibility of the active site of a DNA polymerase while incorporating non-polar "Click-able" nucleotide analogs and emphasizes new insights into rational design guidelines for modified nucleotides.
متن کاملAn Ensemble Click Model for Web Document Ranking
Annually, web search engine providers spend more and more money on documents ranking in search engines result pages (SERP). Click models provide advantageous information for ranking documents in SERPs through modeling interactions among users and search engines. Here, three modules are employed to create a hybrid click model; the first module is a PGM-based click model, the second module in a d...
متن کاملMeasuring and incorporating attitudes toward risk into mathematical programming models : the case of farmers in kavar district iran
متن کامل
Incorporating User Behavior Information in IR Evaluation
Many evaluation measures in Information Retrieval (IR) can be viewed as simple user models. Meanwhile, search logs provide us with information about how real users search. This paper describes our attempts to reconcile click log information with user-centric IR measures, bringing the measures into agreement with the logs. Studying the discount curve of NDCG and RBP leads us to extend them, inco...
متن کاملSimple Personalized Search Based on Long-Term Behavioral Signals
Extensive research has shown that content-based Web result ranking can be significantly improved by considering personal behavioral signals (such as past queries) and global behavioral signals (such as global click frequencies). In this work we present a new approach to incorporating click behavior into document ranking, using ideas of click models as well as learning to rank. We show that by t...
متن کامل