Amazon.com Recommendations: Item-to-Item Collaborative Filtering

Table of Contents

1. Recommender System at Ground Zero
2. Breeds of Recommender System
3. Challenges and Solutions of Recommender System
4. Link to the Paper

Recommender system

• Recommender System is an information filtering system that aims at predicting the preference of rating given to an item by any user, thereby helping users make personalised decision.
• The paper discusses various approaches used in the various recommender systems such as Content based, Collaborative and Hybrid recommender system.
• It also proposes main challenges and the solutions to these recommendation techniques.

Recommender System at Ground Zero

• It

Breeds of Recommender System

Recommender	Description	Advantages	Disadvantages
Content-based Filtering Systems	- Uses information of active users and data about the items. Steps: 1. Gathers content data about the item( author, cost, i.e. metadata) 2. Process data and extract useful features and elements.	- Doesn’t require data of other users. - Has capabilities of recommending items to user with unique taste.	Items are limited to their initial descriptions or features.
Collaborative Filtering Systems	- Uses information about a set of users and their relations with the item to provide recommendations to the active user. - Based on a few customers who are most similar to the active users - uses Cosine Similarity. 1. User Based CF: For each user, compute correlation with other users. For each item, aggregate the rating of the users highly correlated with each user. 2. Item-based CF: For each item, compute correlation with other items. For each user, aggregate his rating of the items highly correlated with each item.	Doesn’t need a representation of items.	The item can’t be recommended to any user until and unless the item is either rated by another user(s) or correlated with other similar items.
Demographic Filtering Systems	- Uses demographic information such as age, gender, education, etc. of people for identifying types of user. - Uses pre-existing knowledge of demographic information about the users and their opinions	- Doesn’t require history of user ratings. - Quick, easy and a straightforward method based on few observations	- Recommendations are stereotypical, as it depends on the assumption that users belong to a certain category. - Security and privacy issues.
Hybrid recommender Systems	- Uses a combination of two or more different recommendation techniques. - Uses both item content and the ratings of all users.	Can overcome various problems caused by a single recommender system	Can be complex to implement as the hybridisation method needs to be chosen carefully.

Challenges and Solutions of Recommender System

• Cold-start
  • a
• Scalability
  • b
• Privacy
  • c
• Sparsity
  • d
• Over-Specialisation (Filter bubble)
  • e

Resources

• Amazon.com Recommendations: Item-to-Item Collaborative Filtering

  G. Linden, B. Smith and J. York, IEEE Internet Computing, vol. 7, no. 1, pp. 76-80, Jan.-Feb. 2003, doi: 10.1109/MIC.2003.1167344.