Implementing your own recommender systems in Python by Agnes Jóhannsdóttir

#artificialintelligence

Nowadays, recommender systems are used to personalize your experience on the web, telling you what to buy, where to eat or even who you should be friends with. People's tastes vary, but generally follow patterns. People tend to like things that are similar to other things they like, and they tend to have similar taste as other people they are close with. Recommender systems try to capture these patterns to help predict what else you might like. E-commerce, social media, video and online news platforms have been actively deploying their own recommender systems to help their customers to choose products more efficiently, which serves win-win strategy.


Hybrid Recommender Systems for Electronic Commerce

AAAI Conferences

System: verifies that it is OK to use the CFRSS. However, after calculating the predicted ratings on items, it finds that none of the items are "good items" (ref.


The Design and Implementation of an Intelligent Online Recommender System

AAAI Conferences

This paper describes the general design and architecture of an intelligent recommendation system aimed mainly at supporting a user in her navigation through the massive amounts of information that she has to cope with in order to find the right information. Alternative recommender system techniques are needed to retrieve quickly high quality recommendations even from a huge amount of data. Singular Value Decomposition-Collaborative Filtering (SVD-CF) methods are the techniques that are used in order to solve some recommender system problems by reducing the dimensionality of the product space, therefore producing better recommendations. Thanks to these techniques we can capture important latent associations between users and items. Also, users can benefit from the extension of their recommendation lists by taking into consideration the purchase of products that tend to be bought together.


Integrating Knowledge-basedand Collaborative-filtering

AAAI Conferences

Entry point: The item that the user chooses as a starting point can be considered a strongly positive preference, since the user is looking for something similar to it. Ending point: The final selection or buying decision can also be considered a positive rating. Tweaking: When a user critiques a returned item and moves on to something different, we can consider this a negative rating. Browsing: If the user navigates to other items in the returned set, we can consider this a weak negative rating: if the user truly liked the item he or she would probably not browse further. These heuristics are somewhat weak, since we sometimes find users who are exploring the system to see what it can do, applying tweaks not to get a specific recommendation, but to see what will come back.


A Fuzzy Community-Based Recommender System Using PageRank

arXiv.org Machine Learning

Recommendation systems are widely used by different user service providers specially those who have interactions with the large community of users. This paper introduces a recommender system based on community detection. The recommendation is provided using the local and global similarities between users. The local information is obtained from communities, and the global ones are based on the ratings. Here, a new fuzzy community detection using the personalized PageRank metaphor is introduced. The fuzzy membership values of the users to the communities are utilized to define a similarity measure. The method is evaluated by using two well-known datasets: MovieLens and FilmTrust. The results show that our method outperforms recent recommender systems.