13,319 research outputs found
Recommender Systems for Online Dating
Users of large online dating sites are confronted with vast numbers of candidates to browse through and communicate with. To help them in their endeavor and to cope with information overload, recommender systems can be utilized.
This thesis introduces reciprocal recommender systems that are aimed towards the domain of online dating. An overview of previously developed methods is presented, and five methods are described in detail, one of which is a novel method developed in this thesis. The five methods are evaluated and compared on a historical data set collected from an online dating website operating in Finland. Additionally, factors influencing the design of online dating recommenders are described, and support for these characteristics are derived from our historical data set and previous research on other data sets.
The empirical comparison of the five methods on different recommendation quality criteria shows that no method is overwhelmingly better than the others and that a trade-off need be taken when choosing one for a live system. However, making that trade-off decision is something that warrants future research, as it is not clear how different criteria affect user experience and likelihood of finding a partner in a live online dating context
Reciprocal Recommendation System for Online Dating
Online dating sites have become popular platforms for people to look for
potential romantic partners. Different from traditional user-item
recommendations where the goal is to match items (e.g., books, videos, etc)
with a user's interests, a recommendation system for online dating aims to
match people who are mutually interested in and likely to communicate with each
other. We introduce similarity measures that capture the unique features and
characteristics of the online dating network, for example, the interest
similarity between two users if they send messages to same users, and
attractiveness similarity if they receive messages from same users. A
reciprocal score that measures the compatibility between a user and each
potential dating candidate is computed and the recommendation list is generated
to include users with top scores. The performance of our proposed
recommendation system is evaluated on a real-world dataset from a major online
dating site in China. The results show that our recommendation algorithms
significantly outperform previously proposed approaches, and the collaborative
filtering-based algorithms achieve much better performance than content-based
algorithms in both precision and recall. Our results also reveal interesting
behavioral difference between male and female users when it comes to looking
for potential dates. In particular, males tend to be focused on their own
interest and oblivious towards their attractiveness to potential dates, while
females are more conscientious to their own attractiveness to the other side of
the line
Online Reciprocal Recommendation with Theoretical Performance Guarantees
A reciprocal recommendation problem is one where the goal of learning is not
just to predict a user's preference towards a passive item (e.g., a book), but
to recommend the targeted user on one side another user from the other side
such that a mutual interest between the two exists. The problem thus is sharply
different from the more traditional items-to-users recommendation, since a good
match requires meeting the preferences of both users. We initiate a rigorous
theoretical investigation of the reciprocal recommendation task in a specific
framework of sequential learning. We point out general limitations, formulate
reasonable assumptions enabling effective learning and, under these
assumptions, we design and analyze a computationally efficient algorithm that
uncovers mutual likes at a pace comparable to those achieved by a clearvoyant
algorithm knowing all user preferences in advance. Finally, we validate our
algorithm against synthetic and real-world datasets, showing improved empirical
performance over simple baselines
Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)
Massive open online courses (MOOC) describe platforms where users with
completely different backgrounds subscribe to various courses on offer. MOOC
forums and discussion boards offer learners a medium to communicate with each
other and maximize their learning outcomes. However, oftentimes learners are
hesitant to approach each other for different reasons (being shy, don't know
the right match, etc.). In this paper, we propose a reciprocal recommender
system which matches learners who are mutually interested in, and likely to
communicate with each other based on their profile attributes like age,
location, gender, qualification, interests, etc. We test our algorithm on data
sampled using the publicly available MITx-Harvardx dataset and demonstrate that
both attribute importance and reciprocity play an important role in forming the
final recommendation list of learners. Our approach provides promising results
for such a system to be implemented within an actual MOOC.Comment: 10 pages, accepted as full paper @ ICWL 201
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