30,082 research outputs found
Recommendation System for News Reader
Recommendation Systems help users to find information and make decisions where they lack the required knowledge to judge a particular product. Also, the information dataset available can be huge and recommendation systems help in filtering this data according to users‟ needs. Recommendation systems can be used in various different ways to facilitate its users with effective information sorting. For a person who loves reading, this paper presents the research and implementation of a Recommendation System for a NewsReader Application using Android Platform. The NewsReader Application proactively recommends news articles as per the reading habits of the user, recorded over a period of time and also recommends the currently trending articles. Recommendation systems and their implementations using various algorithms is the primary area of study for this project. This research paper compares and details popular recommendation algorithms viz. Content based recommendation systems, Collaborative recommendation systems etc. Moreover, it also presents a more efficient Hybrid approach that absorbs the best aspects from both the algorithms mentioned above, while trying to eliminate all the potential drawbacks observed
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning
Traditional recommendation systems rely on past usage data in order to
generate new recommendations. Those approaches fail to generate sensible
recommendations for new users and items into the system due to missing
information about their past interactions. In this paper, we propose a solution
for successfully addressing item-cold start problem which uses model-based
approach and recent advances in deep learning. In particular, we use latent
factor model for recommendation, and predict the latent factors from item's
descriptions using convolutional neural network when they cannot be obtained
from usage data. Latent factors obtained by applying matrix factorization to
the available usage data are used as ground truth to train the convolutional
neural network. To create latent factor representations for the new items, the
convolutional neural network uses their textual description. The results from
the experiments reveal that the proposed approach significantly outperforms
several baseline estimators
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Musical recommendations and personalization in a social network
This paper presents a set of algorithms used for music recommendations and
personalization in a general purpose social network www.ok.ru, the second
largest social network in the CIS visited by more then 40 millions users per
day. In addition to classical recommendation features like "recommend a
sequence" and "find similar items" the paper describes novel algorithms for
construction of context aware recommendations, personalization of the service,
handling of the cold-start problem, and more. All algorithms described in the
paper are working on-line and are able to detect and address changes in the
user's behavior and needs in the real time.
The core component of the algorithms is a taste graph containing information
about different entities (users, tracks, artists, etc.) and relations between
them (for example, user A likes song B with certainty X, track B created by
artist C, artist C is similar to artist D with certainty Y and so on). Using
the graph it is possible to select tracks a user would most probably like, to
arrange them in a way that they match each other well, to estimate which items
from a fixed list are most relevant for the user, and more.
In addition, the paper describes the approach used to estimate algorithms
efficiency and analyze the impact of different recommendation related features
on the users' behavior and overall activity at the service.Comment: This is a full version of a 4 pages article published at ACM RecSys
201
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
IceBreaker: Solving Cold Start Problem for Video Recommendation Engines
Internet has brought about a tremendous increase in content of all forms and,
in that, video content constitutes the major backbone of the total content
being published as well as watched. Thus it becomes imperative for video
recommendation engines such as Hulu to look for novel and innovative ways to
recommend the newly added videos to their users. However, the problem with new
videos is that they lack any sort of metadata and user interaction so as to be
able to rate the videos for the consumers. To this effect, this paper
introduces the several techniques we develop for the Content Based Video
Relevance Prediction (CBVRP) Challenge being hosted by Hulu for the ACM
Multimedia Conference 2018. We employ different architectures on the CBVRP
dataset to make use of the provided frame and video level features and generate
predictions of videos that are similar to the other videos. We also implement
several ensemble strategies to explore complementarity between both the types
of provided features. The obtained results are encouraging and will impel the
boundaries of research for multimedia based video recommendation systems
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