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.
In terms of reachability, the static topology of recommendation networks has been studied for the case of music recommenders. Their corresponding recommendation networks have been found to exhibit heavy-tail degree distributions and small-world properties @cite_27 , implying that they are efficiently navigable with local search algorithms. studied sources (nodes that are never recommended) in music recommendation networks @cite_16 and found that the fraction of sources remained constant independent of the recommendation approach and the network size. This indicates that recommendation networks generally suffer from reachability problems. Celma and Herrera @cite_26 found that collaborative filtering on led to recommendation networks that are prone to a popularity bias, with recommendations biased towards popular songs or artists. They also found that collaborative filtering provided the most accurate recommendations, while at the same time this made it harder for users to navigate to items in the long tail. A hybrid approach and content-based methods provided better novel recommendations. These results suggest that a trade-off exists between accuracy and other evaluation metrics. @cite_13 proposed to measure reachability in the bipartite recommendation graph of users and items as an evaluation measure.
@cite_27 @cite_16 @cite_13 @cite_26
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We study the topology of several music recommendation networks, which arise from relationships between artist, co-occurrence of songs in play lists or experts’ recommendation. The analysis uncovers the emergence of complex network phenomena in these kinds of recommendation networks, built considering artists as nodes and their resemblance as links. We observe structural properties that provide some hints on navigation and possible optimizations on the design of music recommendation systems. Finally, the analysis derived from existing music knowledge sources provides a deeper understanding of the human music similarity perception. To exploit the enormous potential of niche products, modern information systems must support users in exploring digital libraries and online catalogs. A straight-forward way of doing so is to support browsing the available items, which is in general realized by presenting a user the top-N recommendations for each item. However, recent research indicates that most of the niche products reside in the so-called Long Tail, and simple collaborative filtering-based recommender systems alone do not allow to explore these niche products. In this paper we show that it is not only a popularity problem related to the collaborative filtering approach that makes a portion of the elements of a digital library inaccessible via browsing, but also a consequence of the top N-recommendation approach itself. We present a novel framework for studying recommendation algorithms in terms of the ‘jumps’ that they make to connect people to artifacts. This approach emphasizes reachability via an algorithm within the implicit graph structure underlying a recommender dataset and allows us to consider questions relating algorithmic parameters to properties of the datasets. For instance, given a particular algorithm ‘jump,’ what is the average path length from a person to an artifact? Or, what choices of minimum ratings and jumps maintain a connected graph? We illustrate the approach with a common jump called the ‘hammock’ using movie recommender datasets. This paper presents two methods, named Item- and User-centric, to evaluate the quality of novel recommendations. The former method focuses on analyzing the item-based recommendation network. The aim is to detect whether the network topology has any pathology that hinders novel recommendations. The latter, user-centric evaluation, aims at measuring users' perceived quality of novel, previously unknown, recommendations. The results of the experiments, done in the music recommendation context, show that last.fm social recommender, based on collaborative filtering, is prone to popularity bias. This has direct consequences on the topology of the item-based recommendation network. Pure audio content-based methods (CB) are not affected by popularity. However, a user-centric experiment done with 288 subjects shows that even though a social-based approach recommends less novel items than our CB, users' perceived quality is better than those recommended by a pure CB method.