486 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
User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration
Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks.
Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion.
Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data
Latent Geometry Inspired Graph Dissimilarities Enhance Affinity Propagation Community Detection in Complex Networks
Affinity propagation is one of the most effective unsupervised pattern
recognition algorithms for data clustering in high-dimensional feature space.
However, the numerous attempts to test its performance for community detection
in complex networks have been attaining results very far from the state of the
art methods such as Infomap and Louvain. Yet, all these studies agreed that the
crucial problem is to convert the unweighted network topology in a
'smart-enough' node dissimilarity matrix that is able to properly address the
message passing procedure behind affinity propagation clustering. Here we
introduce a conceptual innovation and we discuss how to leverage network latent
geometry notions in order to design dissimilarity matrices for affinity
propagation community detection. Our results demonstrate that the latent
geometry inspired dissimilarity measures we design bring affinity propagation
to equal or outperform current state of the art methods for community
detection. These findings are solidly proven considering both synthetic
'realistic' networks (with known ground-truth communities) and real networks
(with community metadata), even when the data structure is corrupted by noise
artificially induced by missing or spurious connectivity
From Ranked Lists to Carousels: A Carousel Click Model
Carousel-based recommendation interfaces allow users to explore recommended
items in a structured, efficient, and visually-appealing way. This made them a
de-facto standard approach to recommending items to end users in many real-life
recommenders. In this work, we try to explain the efficiency of carousel
recommenders using a \emph{carousel click model}, a generative model of user
interaction with carousel-based recommender interfaces. We study this model
both analytically and empirically. Our analytical results show that the user
can examine more items in the carousel click model than in a single ranked
list, due to the structured way of browsing. These results are supported by a
series of experiments, where we integrate the carousel click model with a
recommender based on matrix factorization. We show that the combined
recommender performs well on held-out test data, and leads to higher engagement
with recommendations than a traditional single ranked list
An Approach for Link Prediction in Directed Complex Networks based on Asymmetric Similarity-Popularity
Complex networks are graphs representing real-life systems that exhibit
unique characteristics not found in purely regular or completely random graphs.
The study of such systems is vital but challenging due to the complexity of the
underlying processes. This task has nevertheless been made easier in recent
decades thanks to the availability of large amounts of networked data. Link
prediction in complex networks aims to estimate the likelihood that a link
between two nodes is missing from the network. Links can be missing due to
imperfections in data collection or simply because they are yet to appear.
Discovering new relationships between entities in networked data has attracted
researchers' attention in various domains such as sociology, computer science,
physics, and biology. Most existing research focuses on link prediction in
undirected complex networks. However, not all real-life systems can be
faithfully represented as undirected networks. This simplifying assumption is
often made when using link prediction algorithms but inevitably leads to loss
of information about relations among nodes and degradation in prediction
performance. This paper introduces a link prediction method designed explicitly
for directed networks. It is based on the similarity-popularity paradigm, which
has recently proven successful in undirected networks. The presented algorithms
handle the asymmetry in node relationships by modeling it as asymmetry in
similarity and popularity. Given the observed network topology, the algorithms
approximate the hidden similarities as shortest path distances using edge
weights that capture and factor out the links' asymmetry and nodes' popularity.
The proposed approach is evaluated on real-life networks, and the experimental
results demonstrate its effectiveness in predicting missing links across a
broad spectrum of networked data types and sizes
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