Many recommendation algorithms suffer from popularity bias: a small number of popular items being recommended too frequently, while other items get insufficient exposure. Research in this area so far has concentrated on a one-shot representation of this bias, and on algorithms to improve the diversity of individual recommendation lists. In this work, we take a time-sensitive view of popularity bias, in which the algorithm assesses its long-tail coverage at regular intervals, and compensates in the present moment for omissions in the past. In particular, we present a temporal version of the well-known xQuAD diversification algorithm adapted for long-tail recommendation. Experimental results on two public datasets show that our method is more effective in terms of the long-tail coverage and accuracy tradeoff compared to some other existing approaches.
The paper studies the Long Tail problem of recommender systems when many items in the Long Tail have only few ratings, thus making it hard to use them in recommender systems. The approach presented in the paper splits the whole itemset into the head and the tail parts and clusters only the tail items. Then recommendations for the tail items are based on the ratings in these clusters and for the head items on the ratings of individual items. If such partition and clustering are done properly, we show that this reduces the recommendation error rates for the tail items, while maintaining reasonable computational performance. Dozens of markets of all types are in the early stages of a revolution as the Internet and related technologies vastly expand the variety of products that can be produced, promoted and purchased. Although this revolution is based on a simple set of economic and technological drivers, the authors argue that its implications are far-reaching for managers, consumers and the economy as a whole. This article looks at what has been dubbed the "Long Tail" phenomenon, examining how customers derive value from an important characteristic of Internet markets: the ability of online merchants to help consumers locate, evaluate and purchase a far wider range of products than they can typically buy via the traditional brick-and-mortar channels. The article examines the Long Tail from both the supply side and the demand side and identifies several key drivers. On the supply side, the authors point out how e-tailers' expanded, centralized warehousing allows for more offerings, thus making it possible for them to cater to more varied tastes. On the demand side, tools such as search engines, recommender software and sampling tools are allowing customers to find products outside of their geographic area. The authors also look toward the future to discuss second order amplified effects of Long Tail, including the growth of markets serving smaller niches.