Socially Optimal Use of Recommender Systems
نویسندگان
چکیده
We study the efficient allocation of buyers in the presence of recommender systems. A recommender system affects the market in two ways: (i) it creates value by reducing product uncertainty for the customers and hence (ii) its recommendations can be offered as add-ons, which generates informational externalities. We investigate the impact of these factors on the efficient allocation of buyers across different products. We find that the efficient allocation requires that the seller with the recommender system has full market share. If the recommender system is sufficiently effective in reducing uncertainty, it is optimal to have some products to be purchased by a larger group of people than others. The large group consists of customers with flexible tastes.
منابع مشابه
A New WordNet Enriched Content-Collaborative Recommender System
The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommen...
متن کاملEvaluation of recommender systems: A multi-criteria decision making approach
The evaluation and selection of recommender systems is a difficult decision making process. This difficulty is partially due to the large diversity of published evaluation criteria in addition to lack of standardized methods of evaluation. As such, a systematic methodology is needed that explicitly considers multiple, possibly conflicting metrics and assists decision makers to evaluate and find...
متن کاملIncreasing 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 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 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 کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005