4,847 research outputs found
Joint interaction with context operation for collaborative filtering
In recommender systems, the classical matrix factorization model for collaborative filtering only considers joint interactions between users and items. In contrast, context-aware recommender systems (CARS) use contexts to improve recommendation performance. Some early CARS models treat user, item and context equally, unable to capture contextual impact accurately. More recent models perform context operations on users and items separately, leading to “double-counting” of contextual information. This paper proposes a new model, Joint Interaction with Context Operation (JICO), to integrate the joint interaction model with the context operation model, via two layers. The joint interaction layer models interactions between users and items via an interaction tensor. The context operation layer captures contextual information via a contextual operating tensor. We evaluate JICO on four datasets and conduct novel studies, including varying contextual influence and time split recommendation. JICO consistently outperforms competing methods, while providing many useful insights to assist further analysis
A personalized and context-aware news offer for mobile devices
For classical domains, such as movies, recommender systems have proven their usefulness. But recommending news is more challenging due to the short life span of news content and the demand for up-to-date recommendations. This paper presents a news recommendation service with a content-based algorithm that uses features of a search engine for content processing and indexing, and a collaborative filtering algorithm for serendipity. The extension towards a context-aware algorithm is made to assess the information value of context in a mobile environment through a user study. Analyzing interaction behavior and feedback of users on three recommendation approaches shows that interaction with the content is crucial input for user modeling. Context-aware recommendations using time and device type as context data outperform traditional recommendations with an accuracy gain dependent on the contextual situation. These findings demonstrate that the user experience of news services can be improved by a personalized context-aware news offer
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
Context-aware Sequential Recommendation
Since sequential information plays an important role in modeling user
behaviors, various sequential recommendation methods have been proposed.
Methods based on Markov assumption are widely-used, but independently combine
several most recent components. Recently, Recurrent Neural Networks (RNN) based
methods have been successfully applied in several sequential modeling tasks.
However, for real-world applications, these methods have difficulty in modeling
the contextual information, which has been proved to be very important for
behavior modeling. In this paper, we propose a novel model, named Context-Aware
Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix
and transition matrix in conventional RNN models, CA-RNN employs adaptive
context-specific input matrices and adaptive context-specific transition
matrices. The adaptive context-specific input matrices capture external
situations where user behaviors happen, such as time, location, weather and so
on. And the adaptive context-specific transition matrices capture how lengths
of time intervals between adjacent behaviors in historical sequences affect the
transition of global sequential features. Experimental results show that the
proposed CA-RNN model yields significant improvements over state-of-the-art
sequential recommendation methods and context-aware recommendation methods on
two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.Comment: IEEE International Conference on Data Mining (ICDM) 2016, to apea
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Recommendations can greatly benefit from good representations of the user
state at recommendation time. Recent approaches that leverage Recurrent Neural
Networks (RNNs) for session-based recommendations have shown that Deep Learning
models can provide useful user representations for recommendation. However,
current RNN modeling approaches summarize the user state by only taking into
account the sequence of items that the user has interacted with in the past,
without taking into account other essential types of context information such
as the associated types of user-item interactions, the time gaps between events
and the time of day for each interaction. To address this, we propose a new
class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that
can take into account the contextual information both in the input and output
layers and modifying the behavior of the RNN by combining the context embedding
with the item embedding and more explicitly, in the model dynamics, by
parametrizing the hidden unit transitions as a function of context information.
We compare our CRNNs approach with RNNs and non-sequential baselines and show
good improvements on the next event prediction task
A user-centric evaluation of context-aware recommendations for a mobile news service
Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the recommendation process is highly dependent on the application domain, the type of contextual information, and variations in users’ usage behavior in different contextual situations. This paper investigates whether users utilize a mobile news service in different contextual situations and whether the context has an influence on their consumption behavior. Furthermore, the importance of context for the recommendation process is investigated by comparing the user satisfaction with recommendations based on an explicit static profile, content-based recommendations using the actual user behavior but ignoring the context, and context-aware content-based recommendations incorporating user behavior as well as context. Considering the recommendations based on the static profile as a reference condition, the results indicate a significant improvement for recommendations that are based on the actual user behavior. This improvement is due to the discrepancy between explicitly stated preferences (initial profile) and the actual consumption behavior of the user. The context-aware content-based recommendations did not significantly outperform the content-based recommendations in our user study. Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations
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