Personalisation and Collaborative Filtering

Personalization (broadly known as customization) consists of tailoring a service or a product to accommodate specific individuals, sometimes tied to groups or segments of individuals. A wide variety of organizations use personalization to improve customer satisfaction, digital sales conversion, marketing results, branding, and improved website metrics as well as for advertising. Personalization is a key element in social media and recommender systems.

Digital media and internet

Another aspect of personalization is the increasing prevalence of open data on the Web. Many companies make their data available on the Web via APIs, web services, and open data standards. One such example is Ordnance Survey Open Data. Data made available in this way is structured to allow it to be inter-connected and re-used by third parties.

Data available from a user’s personal social graph can be accessed by third-party application software to be suited to fit the personalized web page or information appliance.

Current open data standards on the Web include:

• Attention Profiling Mark-up Language (APML)
• DataPortability
• OpenID
• OpenSocial

Predictive personalization

Predictive personalization is defined as the ability to predict customer behavior, needs or wants and tailor offers and communications very precisely. Social data is one source of providing this predictive analysis, particularly social data that is structured. Predictive personalization is a much more recent means of personalization and can be used well to augment current personalization offerings.

Collaborative filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one.

In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B’s opinion on a different issue than that of a randomly chosen person. For example, a collaborative filtering recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user’s tastes (likes or dislikes). Note that these predictions are specific to the user, but use information gleaned from many users. This differs from the simpler approach of giving an average (non-specific) score for each item of interest, for example based on its number of votes.

In the more general sense, collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets. Collaborative filtering methods have been applied to many different kinds of data including: sensing and monitoring data, such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data, such as financial service institutions that integrate many financial sources; or in electronic commerce and web applications where the focus is on user data, etc. The remainder of this discussion focuses on collaborative filtering for user data, although some of the methods and approaches may apply to the other major applications as well.

Methodology

Collaborative Filtering in Recommender Systems

Collaborative filtering systems have many forms, but many common systems can be reduced to two steps:

1. Look for users who share the same rating patterns with the active user (the user whom the prediction is for).
2. Use the ratings from those like-minded users found in step 1 to calculate a prediction for the active user

This falls under the category of user-based collaborative filtering. A specific application of this is the user-based Nearest Neighbor algorithm.

Alternatively, item-based collaborative filtering (users who bought x also bought y), proceeds in an item-centric manner:

1. Build an item-item matrix determining relationships between pairs of items
2. Infer the tastes of the current user by examining the matrix and matching that user’s data