Context adaptation in interactive recommender systems

Hariri, Negar; Mobasher, Bamshad; Burke, Robin

doi:10.1145/2645710.2645753

Cited by 45 publications

(22 citation statements)

References 10 publications

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“…Bandit's approach can be used to generate context aware online recommendations. Instead of modeling context explicitly through external signals, [13] uses user activity or user feedback to model the context and hence gives the most suitable recommendation in that context. These systems lack sequential evolution of the state and they do not consider the final intent of the user.…”

Section: Related Workmentioning

confidence: 99%

Intent-Aware Contextual Recommendation System

Bhattacharya

Burhanuddin

Sancheti

et al. 2017

2017 IEEE International Conference on Data Mining Workshops (ICDMW)

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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

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Section: Related Workmentioning

confidence: 99%

Intent-Aware Contextual Recommendation System

Bhattacharya

Burhanuddin

Sancheti

et al. 2017

2017 IEEE International Conference on Data Mining Workshops (ICDMW)

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“…The majority of the works in this research area focus on identifying items, such as books, movies, restaurants, and activities of interest to an individual or a group of users. This identification can be accomplished using popular techniques, including collaborative filtering and content-based analysis, as well as state-of-the-art strategies that rely on matrix factorization (Bauer & Nanopoulos, 2014), context adaptation (Hariri, Mobasher, & Burke, 2014), and review analysis (Ling, Lyu, & King, 2014). There is no lack of literature on book recommenders (Ricci et al, 2011).…”

Section: Related Workmentioning

confidence: 99%

Recommending books to be exchanged online in the absence of wish lists

Pera

2017

Asso for Info Science & Tech

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An online exchange system is a web service that allows communities to trade items without the burden of manually selecting them, which saves users' time and effort. Even though online book‐exchange systems have been developed, their services can further be improved by reducing the workload imposed on their users. To accomplish this task, we propose a recommendation‐based book exchange system, called EasyEx, which identifies potential exchanges for a user solely based on a list of items the user is willing to part with. EasyEx is a novel and unique book‐exchange system because unlike existing online exchange systems, it does not require a user to create and maintain a wish list, which is a list of items the user would like to receive as part of the exchange. Instead, EasyEx directly suggests items to users to increase serendipity and as a result expose them to items which may be unfamiliar, but appealing, to them. In identifying books to be exchanged, EasyEx employs known recommendation strategies, that is, personalized mean and matrix factorization, to predict book ratings, which are treated as the degrees of appeal to a user on recommended books. Furthermore, EasyEx incorporates OptaPlanner, which solves constraint satisfaction problems efficiently, as part of the recommendation‐based exchange process to create exchange cycles. Experimental results have verified that EasyEx offers users recommended books that satisfy the users' interests and contributes to the item‐exchange mechanism with a new design methodology.

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“…The recent approach in [31] also focuses on fashion products and aims to identify the theme of a user's session by using factored Markov decision processes (fMDPs). In [12], short-term goals are modeled by a multi-armed bandit algorithm that detects a shift in the user's interest based on implicit feedback signals. In contrast to our work, these two approaches focus only on the short-term user goals whereas our method combines long-term user model with short-term interests.…”

Section: Related Workmentioning

confidence: 99%

Adaptation and Evaluation of Recommendations for Short-term Shopping Goals

Jannach

Lerche

Jugovac

2015

Proceedings of the 9th ACM Conference on Recommender Systems

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An essential characteristic in many e-commerce settings is that website visitors can have very specific short-term shopping goals when they browse the site. Relying solely on longterm user models that are pre-trained on historical data can therefore be insufficient for a suitable next-basket recommendation. Simple "real-time" recommendation approaches based, e.g., on unpersonalized co-occurrence patterns, on the other hand do not fully exploit the available information about the user's long-term preference profile.In this work, we aim to explore and quantify the effectiveness of using and combining long-term models and shortterm adaptation strategies. We conducted an empirical evaluation based on a novel evaluation design and two real-world datasets. The results indicate that maintaining short-term content-based and recency-based profiles of the visitors can lead to significant accuracy increases. At the same time, the experiments show that the choice of the algorithm for learning the long-term preferences is particularly important at the beginning of new shopping sessions.

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Context adaptation in interactive recommender systems

Cited by 45 publications

References 10 publications

Intent-Aware Contextual Recommendation System

Intent-Aware Contextual Recommendation System

Recommending books to be exchanged online in the absence of wish lists

Adaptation and Evaluation of Recommendations for Short-term Shopping Goals

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