1,715 research outputs found
NAIS: Neural Attentive Item Similarity Model for Recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used
for building recommender systems in industrial settings, owing to its
interpretability and efficiency in real-time personalization. It builds a
user's profile as her historically interacted items, recommending new items
that are similar to the user's profile. As such, the key to an item-based CF
method is in the estimation of item similarities. Early approaches use
statistical measures such as cosine similarity and Pearson coefficient to
estimate item similarities, which are less accurate since they lack tailored
optimization for the recommendation task. In recent years, several works
attempt to learn item similarities from data, by expressing the similarity as
an underlying model and estimating model parameters by optimizing a
recommendation-aware objective function. While extensive efforts have been made
to use shallow linear models for learning item similarities, there has been
relatively less work exploring nonlinear neural network models for item-based
CF.
In this work, we propose a neural network model named Neural Attentive Item
Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an
attention network, which is capable of distinguishing which historical items in
a user profile are more important for a prediction. Compared to the
state-of-the-art item-based CF method Factored Item Similarity Model (FISM),
our NAIS has stronger representation power with only a few additional
parameters brought by the attention network. Extensive experiments on two
public benchmarks demonstrate the effectiveness of NAIS. This work is the first
attempt that designs neural network models for item-based CF, opening up new
research possibilities for future developments of neural recommender systems
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
International conference on software engineering and knowledge engineering: Session chair
The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing.
The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome
Information Retrieval using applied Supervised Learning for Personalized E-Commerce
Master's thesis in Computer SciencePersonalized E-Commerce Search Challenge issued by the International Conference on Information and Knowledge Management. By analyzing historical data containing browsing logs, queries, user interactions, and static data in the domain of an online retail service, we attempt to extract patterns and derive features from the data collection that will subsequently improve prediction of relevant products. A selection of supervised learning models will utilize an assembly of these features to be trained for prediction of test data. Prediction is performed on the queries given by the data collection, paired with each product item originally appearing in the query. We experiment with the possible assemblies of features along with the models and compare the results to achieve maximum prediction power. Lastly, the quality of the predictions are evaluated towards a ground truth to yield scores.submittedVersio
Deep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopted in
recommender systems in industry, owing to its strength in user interest
modeling and ease in online personalization. By constructing a user's profile
with the items that the user has consumed, ICF recommends items that are
similar to the user's profile. With the prevalence of machine learning in
recent years, significant processes have been made for ICF by learning item
similarity (or representation) from data. Nevertheless, we argue that most
existing works have only considered linear and shallow relationship between
items, which are insufficient to capture the complicated decision-making
process of users.
In this work, we propose a more expressive ICF solution by accounting for the
nonlinear and higher-order relationship among items. Going beyond modeling only
the second-order interaction (e.g. similarity) between two items, we
additionally consider the interaction among all interacted item pairs by using
nonlinear neural networks. Through this way, we can effectively model the
higher-order relationship among items, capturing more complicated effects in
user decision-making. For example, it can differentiate which historical
itemsets in a user's profile are more important in affecting the user to make a
purchase decision on an item. We treat this solution as a deep variant of ICF,
thus term it as DeepICF. To justify our proposal, we perform empirical studies
on two public datasets from MovieLens and Pinterest. Extensive experiments
verify the highly positive effect of higher-order item interaction modeling
with nonlinear neural networks. Moreover, we demonstrate that by more
fine-grained second-order interaction modeling with attention network, the
performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI
Deep Exploration for Recommendation Systems
Modern recommendation systems ought to benefit by probing for and learning
from delayed feedback. Research has tended to focus on learning from a user's
response to a single recommendation. Such work, which leverages methods of
supervised and bandit learning, forgoes learning from the user's subsequent
behavior. Where past work has aimed to learn from subsequent behavior, there
has been a lack of effective methods for probing to elicit informative delayed
feedback. Effective exploration through probing for delayed feedback becomes
particularly challenging when rewards are sparse. To address this, we develop
deep exploration methods for recommendation systems. In particular, we
formulate recommendation as a sequential decision problem and demonstrate
benefits of deep exploration over single-step exploration. Our experiments are
carried out with high-fidelity industrial-grade simulators and establish large
improvements over existing algorithms
A Personalized Dense Retrieval Framework for Unified Information Access
Developing a universal model that can efficiently and effectively respond to
a wide range of information access requests -- from retrieval to recommendation
to question answering -- has been a long-lasting goal in the information
retrieval community. This paper argues that the flexibility, efficiency, and
effectiveness brought by the recent development in dense retrieval and
approximate nearest neighbor search have smoothed the path towards achieving
this goal. We develop a generic and extensible dense retrieval framework,
called \framework, that can handle a wide range of (personalized) information
access requests, such as keyword search, query by example, and complementary
item recommendation. Our proposed approach extends the capabilities of dense
retrieval models for ad-hoc retrieval tasks by incorporating user-specific
preferences through the development of a personalized attentive network. This
allows for a more tailored and accurate personalized information access
experience. Our experiments on real-world e-commerce data suggest the
feasibility of developing universal information access models by demonstrating
significant improvements even compared to competitive baselines specifically
developed for each of these individual information access tasks. This work
opens up a number of fundamental research directions for future exploration.Comment: Accepted to SIGIR 202
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