The Impact of Demographics (Age and Gender) and Other User-Characteristics on Evaluating Recommender Systems
نویسندگان
چکیده
In this paper we show the importance of considering demographics and other user characteristics when evaluating (research paper) recommender systems. We analyzed 37,572 recommendations delivered to 1,028 users and found that elderly users clicked more often on recommendations than younger ones. For instance, 20-24 years old users achieved click-through rates (CTR) of 2.73% on average while CTR for users between 50 and 54 years was 9.26%. Gender only had a marginal impact (CTR males 6.88%; females 6.67%) but other user characteristics such as whether a user was registered (CTR: 6.95%) or not (4.97%) had a strong impact. Due to the results we argue that future research articles on recommender systems should report detailed data on their users to make results better comparable.
منابع مشابه
Increasing the Accuracy of Recommender Systems Using the Combination of K-Means and Differential Evolution Algorithms
Recommender systems are the systems that try to make recommendations to each user based on performance, personal tastes, user behaviors, and the context that match their personal preferences and help them in the decision-making process. One of the most important subjects regarding these systems is to increase the system accuracy which means how much the recommendations are close to the user int...
متن کاملContext-Aware Recommender Systems: A Review of the Structure Research
Recommender systems are a branch of retrieval systems and information matching, which through identifying the interests and requires of the user, help the users achieve the desired information or service through a massive selection of choices. In recent years, the recommender systems apply describing information in the terms of the user, such as location, time, and task, in order to produce re...
متن کاملUse of Semantic Similarity and Web Usage Mining to Alleviate the Drawbacks of User-Based Collaborative Filtering Recommender Systems
One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-ba...
متن کاملA New Similarity Measure Based on Item Proximity and Closeness for Collaborative Filtering Recommendation
Recommender systems utilize information retrieval and machine learning techniques for filtering information and can predict whether a user would like an unseen item. User similarity measurement plays an important role in collaborative filtering based recommender systems. In order to improve accuracy of traditional user based collaborative filtering techniques under new user cold-start problem a...
متن کاملMerging Similarity and Trust Based Social Networks to Enhance the Accuracy of Trust-Aware Recommender Systems
In recent years, collaborative filtering (CF) methods are important and widely accepted techniques are available for recommender systems. One of these techniques is user based that produces useful recommendations based on the similarity by the ratings of likeminded users. However, these systems suffer from several inherent shortcomings such as data sparsity and cold start problems. With the dev...
متن کامل