Discovering Popular Clicks' Pattern of Teen Users for Query Recommendation

Abstract

Search engines are still the most important gates for information search in internet. In this regard, providing the best response in the shortest time possible to the user's request is still desired. Normally, search engines are designed for adults and few policies have been employed considering teen users. Teen users are more biased in clicking the results list than are adult users. This leads to fewer clicks on the lowly-ranked search results. Such behavior reduces teen users' navigation and result extraction skills. With an increase in information load and in teen's demands, lack of efficient methods leads to inefficiency of search engines regarding teen users. For the purpose, this study discovers teen users' search behavior and its application in yielding an improved search is strongly recommended. In this way, the pattern of teen users' popular clicks is identified from a large search log through mining of users' search transactions based on the frequency and similarity of the clicks in the search log. Then, using binary classification, the closest query into the teen user's desired one is identified. To discover teen users' behavior, we took advantage of the AOL query log. System efficiency was examined on the AOL query search log. Results reveal that click pattern improves approaching the query to the one desired by teen users. Generally, this study can demonstrate that in data recovery, application of click behavior and its binary classification can result in improved access of teen users to their desired results.