385 research outputs found
Improving Reachability and Navigability in Recommender Systems
In this paper, we investigate recommender systems from a network perspective
and investigate recommendation networks, where nodes are items (e.g., movies)
and edges are constructed from top-N recommendations (e.g., related movies). In
particular, we focus on evaluating the reachability and navigability of
recommendation networks and investigate the following questions: (i) How well
do recommendation networks support navigation and exploratory search? (ii) What
is the influence of parameters, in particular different recommendation
algorithms and the number of recommendations shown, on reachability and
navigability? and (iii) How can reachability and navigability be improved in
these networks? We tackle these questions by first evaluating the reachability
of recommendation networks by investigating their structural properties.
Second, we evaluate navigability by simulating three different models of
information seeking scenarios. We find that with standard algorithms,
recommender systems are not well suited to navigation and exploration and
propose methods to modify recommendations to improve this. Our work extends
from one-click-based evaluations of recommender systems towards multi-click
analysis (i.e., sequences of dependent clicks) and presents a general,
comprehensive approach to evaluating navigability of arbitrary recommendation
networks
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Solving the apparent diversity-accuracy dilemma of recommender systems
Recommender systems use data on past user preferences to predict possible
future likes and interests. A key challenge is that while the most useful
individual recommendations are to be found among diverse niche objects, the
most reliably accurate results are obtained by methods that recommend objects
based on user or object similarity. In this paper we introduce a new algorithm
specifically to address the challenge of diversity and show how it can be used
to resolve this apparent dilemma when combined in an elegant hybrid with an
accuracy-focused algorithm. By tuning the hybrid appropriately we are able to
obtain, without relying on any semantic or context-specific information,
simultaneous gains in both accuracy and diversity of recommendations.Comment: 10 pages, 9 figures, 4 tables (final version with supporting
information included
Evaluating recommender systems from the user's perspective: survey of the state of the art
A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users' perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS's ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users' adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system's recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system's overall perceptive qualities and how these qualities influence users' behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing method
Result Diversification in Search and Recommendation: A Survey
Diversifying return results is an important research topic in retrieval
systems in order to satisfy both the various interests of customers and the
equal market exposure of providers. There has been growing attention on
diversity-aware research during recent years, accompanied by a proliferation of
literature on methods to promote diversity in search and recommendation.
However, diversity-aware studies in retrieval systems lack a systematic
organization and are rather fragmented. In this survey, we are the first to
propose a unified taxonomy for classifying the metrics and approaches of
diversification in both search and recommendation, which are two of the most
extensively researched fields of retrieval systems. We begin the survey with a
brief discussion of why diversity is important in retrieval systems, followed
by a summary of the various diversity concerns in search and recommendation,
highlighting their relationship and differences. For the survey's main body, we
present a unified taxonomy of diversification metrics and approaches in
retrieval systems, from both the search and recommendation perspectives. In the
later part of the survey, we discuss the open research questions of
diversity-aware research in search and recommendation in an effort to inspire
future innovations and encourage the implementation of diversity in real-world
systems.Comment: 20 page
A Comparative Study of Recommendation Systems
Recommendation Systems or Recommender Systems have become widely popular due to surge of information at present time and consumer centric environment. Researchers have looked into a wide range of recommendation systems leveraging a wide range of algorithms. This study investigates three popular recommendation systems in existence, Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation system. The famous MovieLens dataset was utilized for the purpose of this study. The evaluation looked into both quantitative and qualitative aspects of the recommendation systems. We found that from both the perspectives, the hybrid recommendation system performs comparatively better than standalone Collaborative Filtering or Content-Based Filtering recommendation syste
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