24,531 research outputs found
Knowledge-aware Complementary Product Representation Learning
Learning product representations that reflect complementary relationship
plays a central role in e-commerce recommender system. In the absence of the
product relationships graph, which existing methods rely on, there is a need to
detect the complementary relationships directly from noisy and sparse customer
purchase activities. Furthermore, unlike simple relationships such as
similarity, complementariness is asymmetric and non-transitive. Standard usage
of representation learning emphasizes on only one set of embedding, which is
problematic for modelling such properties of complementariness. We propose
using knowledge-aware learning with dual product embedding to solve the above
challenges. We encode contextual knowledge into product representation by
multi-task learning, to alleviate the sparsity issue. By explicitly modelling
with user bias terms, we separate the noise of customer-specific preferences
from the complementariness. Furthermore, we adopt the dual embedding framework
to capture the intrinsic properties of complementariness and provide geometric
interpretation motivated by the classic separating hyperplane theory. Finally,
we propose a Bayesian network structure that unifies all the components, which
also concludes several popular models as special cases. The proposed method
compares favourably to state-of-art methods, in downstream classification and
recommendation tasks. We also develop an implementation that scales efficiently
to a dataset with millions of items and customers
Recommendation, collaboration and social search
This chapter considers the social component of interactive information retrieval: what is the role of other people in searching and browsing? For simplicity we begin by considering situations without computers. After all, you can interactively retrieve information without a computer; you just have to interact with someone or something else. Such an analysis can then help us think about the new forms of collaborative interactions that extend our conceptions of information search, made possible by the growth of networked ubiquitous computing technology.
Information searching and browsing have often been conceptualized as a solitary activity, however they always have a social component. We may talk about 'the' searcher or 'the' user of a database or information resource. Our focus may be on individual uses and our research may look at individual users. Our experiments may be designed to observe the behaviors of individual subjects. Our models and theories derived from our empirical analyses may focus substantially or exclusively on an individual's evolving goals, thoughts, beliefs, emotions and actions. Nevertheless there are always social aspects of information seeking and use present, both implicitly and explicitly.
We start by summarizing some of the history of information access with an emphasis on social and collaborative interactions. Then we look at the nature of recommendations, social search and interfaces to support collaboration between information seekers. Following this we consider how the design of interactive information systems is influenced by their social elements
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
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
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