Recommender systems: from algorithms to user experience

Abstract

Since their introduction in the early 1990's, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.

Systematic Evaluation of Social Recommendation Systems: Challenges and Future

“…The space where these key elements make sense is called community. Konstan [2] discussed 8 dimensions of analysis for Recommendation system. They are various aspects to these systems, which makes the understanding and functioning of it easier to researcher.…”

“…In mid-90 a lot of research was done to improve Collaborative Filtering (CF) [2], [3] [4], [5] , [6] one of the most popular methods of recommendation, and even now. One of the major problem with CF is Cold start problem which occurs due to initial lack of ratings to make any reliable recommendation.…”

“…This led to the second phase of the evolution in recommendation system. With the rapid expansion of Web 2.0, these systems incorporated social information [8] [9], [10], [11], [12] along with information used in traditional recommendation system, leading to the development of Social based Recommendation systems [13], [14], [15], [2] [16], [17] [18]. This social information was related to the virtual social circle of the user.…”

User-Centric vs. System-Centric Evaluation of Recommender Systems

“…─ HotelAvg is a non-personalized algorithm and presents hotels in decreasing order of average user rating [15]. This is the default ranking option adopted in our study when the user does not receive personalized recommendations.…”

“…The hybrid algorithm tested in this study cannot be evaluated with error metrics since it does not compute actual ratings [15]. Hence we have considered only accuracy metrics.…”