منابع مشابه
Challenging the Long Tail Recommendation
The success of “infinite-inventory” retailers such as Amazon.com and Netflix has been largely attributed to a “long tail” phenomenon. Although the majority of their inventory is not in high demand, these niche products, unavailable at limited-inventory competitors, generate a significant fraction of total revenue in aggregate. In addition, tail product availability can boost head sales by offer...
متن کاملMusic Recommendation and the Long Tail
Using a dataset of 7 billion recent submissions to the Last.fm Scrobble API, we investigate possible popularity bias in Last.fm’s recommendations and streaming radio services. In particular we compare the recent listening of users who listen regularly to Last.fm streaming services with those who listen less often or never. Finally we describe a new service explicitly designed to make recommenda...
متن کاملRecommendation Networks and the Long Tail of Electronic Commerce
The program (crawler) which collects the graph starts at a popular book. It then traverses the co-purchase network using a depth-first search. Intuitively, in a depth-first search, one starts at the root (in our case, the one popular book chosen) and traverses the graph as far as possible along each branch before backtracking. At each page, the crawler gathers and records information for the bo...
متن کاملValue-Aware Item Weighting for Long-Tail Recommendation
Many recommender systems suffer from the popularity bias problem: popular items are being recommended frequently while less popular, niche products, are recommended rarely if not at all. However, those ignored products are exactly the products that businesses need to find customers for and their recommendations would be more beneficial. In this paper, we examine an item weighting approach to im...
متن کاملEntity Based Query Recommendation for Long-Tail Queries
Query recommendation, which suggests related queries to search engine users, has attracted a lot of attention in recent years. Most of the existing solutions, which perform analysis of users’ search history (or query logs), are often insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study the use of entities found in queries to provide recommendatio...
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ژورنال
عنوان ژورنال: Proceedings of the VLDB Endowment
سال: 2012
ISSN: 2150-8097
DOI: 10.14778/2311906.2311916