Personalizable and Interactive Sequence Recommender System
نویسنده
چکیده
Copyright held by the owner/author(s). CHI’18 Extended Abstracts, April 21–26, 2018, Montreal, QC, Canada. ACM 978-1-4503-5621-3/18/04. https://doi.org/10.1145/3170427.3188506 Abstract Sequence recommender systems assist people in making decisions, such as which product to purchase and what places to visit on vacation. Despite their ubiquity, most sequence recommender systems are black boxes and do not offer justifications for their recommendations or provide user controls for steering the algorithm. In this paper, we design and develop an interactive sequence recommender system (SeRIES) prototype that uses visualizations to explain and justify the recommendations and provides controls so that users may personalize the recommendations. We conducted a user study comparing SeRIES to a black-box system with 12 participants using real visitor trajectory data in Melbourne and show that SeRIES users are more informed about how the recommendations are generated, more confident in following the recommendations, and more engaged in the decision process.
منابع مشابه
Effect of Rating Time for Cold Start Problem in Collaborative Filtering
Cold start is one of the main challenges in recommender systems. Solving sparsechallenge of cold start users is hard. More cold start users and items are new. Sine many general methods for recommender systems has over fittingon cold start users and items, so recommendation to new users and items is important and hard duty. In this work to overcome sparse problem, we present a new method for rec...
متن کاملAn Effective Algorithm in a Recommender System Based on a Combination of Imperialist Competitive and Firey Algorithms
With the rapid expansion of the information on the Internet, recommender systems play an important role in terms of trade and research. Recommender systems try to guess the user's way of thinking, using the in-formation of user's behavior or similar users and their views, to discover and then propose a product which is the most appropriate and closest product of user's interest. In the past dec...
متن کاملInvestigating the Effects of Off-Task Personalization on System Performance and Attitudes within a Game-Based Environment
The current study investigates the relation between personalizable feature use, attitudes, and in-system performance in the context of the game-based system, iSTART-ME. This analysis focuses on a subset (n=40) of a larger study (n=126) conducted with high school students. The results revealed a positive relation between students’ frequency of interactions with personalizable features and their ...
متن کاملA New WordNet Enriched Content-Collaborative Recommender System
The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommen...
متن کاملA Grouping Hotel Recommender System Based on Deep Learning and Sentiment Analysis
Recommender systems are important tools for users to identify their preferred items and for businesses to improve their products and services. In recent years, the use of online services for selection and reservation of hotels have witnessed a booming growth. Customer’ reviews have replaced the word of mouth marketing, but searching hotels based on user priorities is more time-consuming. This s...
متن کامل