User click behaviors reflect his preference in Web search processing objectively, and it is very important to give a proper interpretation of user click for improving search results. Previous click models explore the relationship between user examines and latent clicks web document obtained by search result page via multiple-click model, such as the independent click model(ICM) or the dependent...

This study tests how peripheral auditory processing and spectral dominance impact lateralization of precedence effect (PE) stimuli consisting of a pair of leading and lagging clicks. Predictions from a model whose parameters were set from established physiological results were tested with specific behavioral experiments. To generate predictions, an auditory nerve model drove a binaural, cross c...

Endicott College submitted three runs to Task 1 of the 2014 TREC Session Track. All runs reranked the baseline runs provided by the track organizers. One of the runs made use of a click graph to re-rank results for RL1, RL2, and RL3. The other two used relevance models computed over snippets from the session, and boosted their RL3 run using click graph recommendations. In the absence of clicks ...

Search engine query log is a valuable information source to analyze the users’ interests and preferences. In existing work, click graph is intensively utilized to analyze the information in query log. However, click graph is usually plagued by low information coverage, failure of capturing the diverse types of co-occurrence and the incapability of discovering the latent semantics in data. In th...

We present autonomous bidding strategies for ad auctions, first for a stylized problem, and then for the more realistic Trading Agent Competition for Ad Auctions (TAC AA)—a simulated market environment that tries to capture some of the complex dynamics of bidding in ad auctions. We decompose the agent’s problem into a modeling subproblem, where we estimate values such as click probability and c...

We propose the usefulness of probabilistic relational methods for modeling user behavior at web sites. Web logs (aka "click streams"), server logs, and other data sources, taken as datasets for traditional machine learning algorithms, violate the iid assumption of most algorithms. Requests ("clicks") are not independent within a session, sessions for a visitor are not independent of one another...