80,120 research outputs found
Tourist Attractions Recommendation based on Attention Knowledge Graph Convolution Network
The recommendation algorithm based on knowledge graphs is at a relatively
mature stage. However, there are still some problems in the recommendation of
specific areas. For example, in the tourism field, selecting suitable tourist
attraction attributes process is complicated as the recommendation basis for
tourist attractions. In this paper, we propose the improved Attention Knowledge
Graph Convolution Network model, named (Att-KGCN), which automatically
discovers the neighboring entities of the target scenic spot semantically. The
attention layer aggregates relatively similar locations and represents them
with an adjacent vector. Then, according to the tourist's preferred choices,
the model predicts the probability of similar spots as a recommendation system.
A knowledge graph dataset of tourist attractions used based on tourism data on
Socotra Island-Yemen. Through experiments, it is verified that the Attention
Knowledge Graph Convolution Network has a good effect on the recommendation of
tourist attractions and can make more recommendations for tourists' choices.Comment: This manuscript contains 7 pages, 4 figures. We have submitted the
manuscript to The 3rd International Conference on Emerging Smart Technologies
and Applications (eSmarTA2023
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
Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations
Collaborative filtering algorithms haven been widely used in recommender
systems. However, they often suffer from the data sparsity and cold start
problems. With the increasing popularity of social media, these problems may be
solved by using social-based recommendation. Social-based recommendation, as an
emerging research area, uses social information to help mitigate the data
sparsity and cold start problems, and it has been demonstrated that the
social-based recommendation algorithms can efficiently improve the
recommendation performance. However, few of the existing algorithms have
considered using multiple types of relations within one social network. In this
paper, we investigate the social-based recommendation algorithms on
heterogeneous social networks and proposed Hete-CF, a Social Collaborative
Filtering algorithm using heterogeneous relations. Distinct from the exiting
methods, Hete-CF can effectively utilize multiple types of relations in a
heterogeneous social network. In addition, Hete-CF is a general approach and
can be used in arbitrary social networks, including event based social
networks, location based social networks, and any other types of heterogeneous
information networks associated with social information. The experimental
results on two real-world data sets, DBLP (a typical heterogeneous information
network) and Meetup (a typical event based social network) show the
effectiveness and efficiency of our algorithm
Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations
The work described here was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61373051; the National Science and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory for Symbolic Computation and Knowledge Engineering, Ministry of Education, China, and the UK Economic & Social Research Council (ESRC); award reference: ES/M001628/1.Preprin
- …