Evaluating Recommender Systems with User Experiments
نویسندگان
چکیده
Proper evaluation of the user experience of recommender systems requires conducting user experiments. This chapter is a guideline for students and researchers aspiring to conduct user experiments with their recommender systems. It first covers the theory of user-centric evaluation of recommender systems, and gives an overview of recommender system aspects to evaluate. It then provides a detailed practical description of how to conduct user experiments, covering the following topics: formulating hypotheses, sampling participants, creating experimental manipulations, measuring subjective constructs with questionnaires, and statistically evaluating the results.
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 کاملImproving the performance of recommender systems in the face of the cold start problem by analyzing user behavior on social network
The goal of recommender system is to provide desired items for users. One of the main challenges affecting the performance of recommendation systems is the cold-start problem that is occurred as a result of lack of information about a user/item. In this article, first we will present an approach, uses social streams such as Twitter to create a behavioral profile, then user profiles are clusteri...
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 کاملEnsemble-based Top-k Recommender System Considering Incomplete Data
Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user-interest (Top-k recommendation problem). Demanding sufficient ratings to make robust predictions and suggesting qualified recommendations are two si...
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 کامل