359,428 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
Graph-RAT: Combining data sources in music recommendation systems
The complexity of music recommendation systems has increased rapidly in recent years, drawing upon different sources of information: content analysis, web-mining, social tagging, etc. Unfortunately, the tools to scientifically evaluate such integrated systems are not readily available; nor are the base algorithms available. This article describes Graph-RAT (Graph-based Relational Analysis Toolkit), an open source toolkit that provides a framework for developing and evaluating novel hybrid systems. While this toolkit is designed for music recommendation, it has applications outside its discipline as well. An experimentâindicative of the sort of procedure that can be configured using the toolkitâis provided to illustrate its usefulness
Reducing Offline Evaluation Bias in Recommendation Systems
Recommendation systems have been integrated into the majority of large online
systems. They tailor those systems to individual users by filtering and ranking
information according to user profiles. This adaptation process influences the
way users interact with the system and, as a consequence, increases the
difficulty of evaluating a recommendation algorithm with historical data (via
offline evaluation). This paper analyses this evaluation bias and proposes a
simple item weighting solution that reduces its impact. The efficiency of the
proposed solution is evaluated on real world data extracted from Viadeo
professional social network.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn
2014), Bruxelles : Belgium (2014
âDo you trust me?â â A Structured Evaluation of Trust and Social Recommendation Agents
Recommender systems are considered as useful software that helps users in screening and evaluating products. The fact that users do not know how these systems make decisions leads to an information asymmetry. Thus, users need to trust if they want to take over systemsâ recommendations. Applying social interfaces has been suggested as helpful extensions of recommender systems to increase trust. These are called (Social) Recommendation Agents. While many articles and implementations can be found in the field of e-commerce, we believe that Recommendation Agents can be applied to other contexts, too. However, a structured evaluation of contexts and design dimensions for Recommendation Agents is lacking. In this study, first, we give an overview of design dimensions for Recommendation Agents. Second, we explore previous research on trust and Recommendation Agents by means of a structured literature review. Finally, based on the resulting overview, we highlight three major areas for future research
Formalizing Multimedia Recommendation through Multimodal Deep Learning
Recommender systems (RSs) offer personalized navigation experiences on online
platforms, but recommendation remains a challenging task, particularly in
specific scenarios and domains. Multimodality can help tap into richer
information sources and construct more refined user/item profiles for
recommendations. However, existing literature lacks a shared and universal
schema for modeling and solving the recommendation problem through the lens of
multimodality. This work aims to formalize a general multimodal schema for
multimedia recommendation. It provides a comprehensive literature review of
multimodal approaches for multimedia recommendation from the last eight years,
outlines the theoretical foundations of a multimodal pipeline, and demonstrates
its rationale by applying it to selected state-of-the-art approaches. The work
also conducts a benchmarking analysis of recent algorithms for multimedia
recommendation within Elliot, a rigorous framework for evaluating recommender
systems. The main aim is to provide guidelines for designing and implementing
the next generation of multimodal approaches in multimedia recommendation
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