{'fraction_non_alphanumeric': 0.021657953696788648, 'fraction_numerical': 0.0014936519790888724, 'mean_word_length': 5.090909090909091, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Predicting query reformulation type from user behavior

This paper proposes a method to discover how a user's search intent changes using his/her behavior during a Web search. A Web search user has a particular search intent and formulates search queries according to that intent. It is, however, a difficult task for the user to formulate a optimal query, a single query able to find documents which completely satisfy his/her information need, by himself. After issuing the initial query, the user usually examines the search results, and modifies his/her initial query. The subsequent queries may be a query of Specialization type, Parallel Move type and so on. By recording these subsequent queries and the corresponding user behavior (including eye-gazing behavior), the present work tries to find the relationship between the user's query reformulation and user's behavior. The proposed method constructs a SVM classifier from the behavior log data obtained from the search and browsing processes. Our experimental results show that the proposed method can classify the next query reformulation into five categories using only the current search behavior data with about 41 % accuracy, greater than the baseline methods. We also analyze which and to what extent the user's behavior data is useful for predicting query reformulations.