An Advertisement Collaborative Recommendation Algorithm Without Position Bias
Huo Xiaoju · Jisuanji gongcheng · 2014
In an advertisement recommendation system,an advertisement which has a low relevance with the query has a high Click-through Rate(CTR)because of the high position where it is shown. Advertisement-query relevance which attracts users to click advertisement plays an important role at advertisement recommendation,and a wrong relevance,for example,CTR with position bias,may lead to a bad recommendation to user and loss of interest. Aiming at these problems,this paper proposes an advertisement collaborative recommendation algorithm without position bias. It finds similar page with other neighbors page by collaborative filtering technology to realize accurate advertising recommended.An experiment on tencent soso data about advertisement logs shows that this algorithm has a better recommendation result(at least40% higher)of higher recall,precision and F-measure than baseline traditional Collaborative Filtering(CF)algorithm without the effect of position bias,and it has good advertising recommendation effect.