Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In b search, information about times beten user actions has been shown to be a good indicator of users' satisfaction with the search results. Existing work uses the mean values of the observed times, or fits probability distributions to the observed times. This implies a context-independence assumption that the time elapsed beten a pair of user actions does not depend on the context, in which the first action takes place.  validate this assumption using logs of a commercial b search engine and discover that it does not always hold. For beten 37% to 80% of query-result pairs, depending on the number of observations, the distributions of click dll times have statistically significant differences in query sessions for which a given result (i) is the first item to be clicked and (ii) is not the first. To account for this context bias effect,  propose a context-aware time model (CATM). The CATM allows us (i) to predict times beten user actions in contexts, in which these actions re not observed, and (ii) to compute context-independent estimates of the times by predicting them in predefined contexts. Our experimental results show that the CATM provides better means than existing methods to predict and interpret times beten user actions.
['Alexey Borisov', 'Ilya Markov', 'Maarten de Rijke', 'Pavel Serdyukov']
I am writing a research paper on a idea A Context-aware Time Model for Web Search
[{"from": "human", "value": "I am writing a research paper on a idea A Context-aware Time Model for Web Search"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In b search, information about times beten user actions has been shown to be a good indicator of users' satisfaction with the search results. Existing work uses the mean values of the observed times, or fits probability distributions to the observed times. This implies a context-independence assumption that the time elapsed beten a pair of user actions does not depend on the context, in which the first action takes place.  validate this assumption using logs of a commercial b search engine and discover that it does not always hold. For beten 37% to 80% of query-result pairs, depending on the number of observations, the distributions of click dll times have statistically significant differences in query sessions for which a given result (i) is the first item to be clicked and (ii) is not the first. To account for this context bias effect,  propose a context-aware time model (CATM). The CATM allows us (i) to predict times beten user actions in contexts, in which these actions re not observed, and (ii) to compute context-independent estimates of the times by predicting them in predefined contexts. Our experimental results show that the CATM provides better means than existing methods to predict and interpret times beten user actions."}]