9 research outputs found
Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders
In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes
Toward a Robust Diversity-Based Model to Detect Changes of Context
Being able to automatically and quickly understand the user context during a
session is a main issue for recommender systems. As a first step toward
achieving that goal, we propose a model that observes in real time the
diversity brought by each item relatively to a short sequence of consultations,
corresponding to the recent user history. Our model has a complexity in
constant time, and is generic since it can apply to any type of items within an
online service (e.g. profiles, products, music tracks) and any application
domain (e-commerce, social network, music streaming), as long as we have
partial item descriptions. The observation of the diversity level over time
allows us to detect implicit changes. In the long term, we plan to characterize
the context, i.e. to find common features among a contiguous sub-sequence of
items between two changes of context determined by our model. This will allow
us to make context-aware and privacy-preserving recommendations, to explain
them to users. As this is an ongoing research, the first step consists here in
studying the robustness of our model while detecting changes of context. In
order to do so, we use a music corpus of 100 users and more than 210,000
consultations (number of songs played in the global history). We validate the
relevancy of our detections by finding connections between changes of context
and events, such as ends of session. Of course, these events are a subset of
the possible changes of context, since there might be several contexts within a
session. We altered the quality of our corpus in several manners, so as to test
the performances of our model when confronted with sparsity and different types
of items. The results show that our model is robust and constitutes a promising
approach.Comment: 27th IEEE International Conference on Tools with Artificial
Intelligence (ICTAI 2015), Nov 2015, Vietri sul Mare, Ital
Intent-Aware Contextual Recommendation System
Recommender systems take inputs from user history, use an internal ranking
algorithm to generate results and possibly optimize this ranking based on
feedback. However, often the recommender system is unaware of the actual intent
of the user and simply provides recommendations dynamically without properly
understanding the thought process of the user. An intelligent recommender
system is not only useful for the user but also for businesses which want to
learn the tendencies of their users. Finding out tendencies or intents of a
user is a difficult problem to solve.
Keeping this in mind, we sought out to create an intelligent system which
will keep track of the user's activity on a web-application as well as
determine the intent of the user in each session. We devised a way to encode
the user's activity through the sessions. Then, we have represented the
information seen by the user in a high dimensional format which is reduced to
lower dimensions using tensor factorization techniques. The aspect of intent
awareness (or scoring) is dealt with at this stage. Finally, combining the user
activity data with the contextual information gives the recommendation score.
The final recommendations are then ranked using filtering and collaborative
recommendation techniques to show the top-k recommendations to the user. A
provision for feedback is also envisioned in the current system which informs
the model to update the various weights in the recommender system. Our overall
model aims to combine both frequency-based and context-based recommendation
systems and quantify the intent of a user to provide better recommendations.
We ran experiments on real-world timestamped user activity data, in the
setting of recommending reports to the users of a business analytics tool and
the results are better than the baselines. We also tuned certain aspects of our
model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big
Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining
(ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field
cannot be longer than 1,920 characters," the abstract appearing here is
slightly shorter than the one in the PDF fil
Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders
In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes