User:Nova2358/Collaborative filtering edit history

Collaborative filtering (CF) is a technique used by some recommender systems. Collaborative filtering has two senses, a narrow one and a more general one. In general, 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 2.0 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.

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 x than to have the opinion on x of a person chosen randomly. 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.

Introduction
The growth of the Internet has made it much more difficult to effectively extract useful information from all the available online information. The overwhelming amount of data necessitates mechanisms for the efficient information filtering. One of the techniques used for dealing with this problem is called collaborative filtering.

The motivation for collaborative filtering comes from the idea that people often get the best recommendation from someone with similar taste. Collaborative filtering explores techniques for matching people with similar interests and making recommendations on this basis.

Collaborative filtering algorithms often require (1) users’ active participation. (2) an easy way to represent users’ interests to the system, and (3) algorithms that are able to match people with similar interests.

Typically, the workflow of a collaborative filtering system is: A key of Collaborative filtering is how to combine and weight the preferences of your neighbors. Sometimes, user can immediately rate the recommended items if they do not interest her. As a result, the system gains an increasingly accurate representation of your preferences over time.
 * 1) A user express her preferences by rating items (eg. books, movies or CDs) of the system. These ratings can be viewed as an approximate representation of your interest that domain.
 * 2) The system matches this user’s ratings against other users’  and find the people with most “similar” tastes.
 * 3) With similar users, the system recommends items that the similar users have rated highly but not yet being rated by this user (presumably the absence of rating is often considered as the unfamiliarity of an item)

Methodology
Collaborative filtering systems have many forms, but many common systems can be reduced to two steps: This falls under the category of user-based collaborative filtering. A specific application of this is the user-based Nearest Neighbor algorithm.
 * 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

Alternatively, item-based collaborative filtering invented by Amazon.com (users who bought x also bought y), proceeds in an item-centric manner: See, for example, the Slope One item-based collaborative filtering family.
 * 1) Build an item-item matrix determining relationships between pairs of items
 * 2) Using the matrix, and the data on the current user, infer his taste

Another form of collaborative filtering can be based on implicit observations of normal user behavior (as opposed to the artificial behavior imposed by a rating task). In these systems you observe what a user has done together with what all users have done (what music they have listened to, what items they have bought) and use that data to predict the user's behavior in the future or to predict how a user might like to behave if only they were given a chance. These predictions then have to be filtered through business logic to determine how these predictions might affect what a business system ought to do. It is, for instance, not useful to offer to sell somebody some music if they already have demonstrated that they own that music or, considering another example, it is not useful to suggest more travel guides for Paris to someone who already bought a travel guide for this city.

Relying on a scoring or rating system which is averaged across all users ignores specific demands of a user, and is particularly poor in tasks where there is large variation in interest, for example in the recommendation of music. However, there are other methods to combat information explosion, for example web search, data clustering, and more.

Memory-Based
This mechanism uses user rating data to compute similarity between users or items. This is used for making recommendations. This was the earlier mechanism and is used in many commercial systems. It is easy to implement and is effective. Typical examples of this mechanism are neighborhood based CF and item-based/user-based top-N recommendations. For example, in user based approaches, the value of ratings user u gives to item i is calculated as an aggregation of some similar users rating to the item:
 * $$r_{u,i} = agrr_{u^\prime \in U} r_{u^\prime, i}$$

where U denotes the set of top N users that are most similar to user u who rated item i. Some examples of the aggregation function includes:
 * $$r_{u,i} = \frac{1}{N}\sum\limits_{u^\prime \in U}r_{u^\prime, i}$$
 * $$r_{u,i} = k\sum\limits_{u^\prime \in U}sim(u,u^\prime)r_{u^\prime, i}$$
 * $$r_{u,i} = \bar{r_u} + k\sum\limits_{u^\prime \in U}sim(u,u^\prime)(r_{u^\prime, i}-\bar{r_{u^\prime}} )$$

where k is a normalizing factor defined as $$k =1/\sum_{u^\prime \in U}|sim(u,u^\prime)| $$. and $$\bar{r_u}$$ is the average rating of user u for all the items he rated.

The neighborhood-based algorithm calculates the similarity between two users or items, produces a prediction for the user taking the weighted average of all the ratings. Similarity computation between items or users is an important part of this approach. Multiple mechanisms such as Pearson correlation and vector cosine based similarity are used for this.

The Pearson correlation similarity of two users x, y is defined as
 * $$ sim(x,y) = \frac{\sum\limits_{i \in I_{xy}}(r_{x,i}-\bar{r_x})(r_{y,i}-\bar{r_y})}{\sqrt{\sum\limits_{i \in I_{xy}}(r_{x,i}-\bar{r_x})^2\sum\limits_{i \in I_{xy}}(r_{y,i}-\bar{r_y})^2}} $$

where Ixy is the set of items both rated by user x and user y.

The cosine-based approach defines the cosine-similarity between two users x and y as:
 * $$sim(x,y) = cos(\vec x,\vec y) = \frac{\vec x \cdot \vec y}{||\vec x||_2 \times ||\vec y||_2} = \frac{\sum\limits_{i \in I_{xy}}r_{x,i}r_{y,i}}{\sqrt{\sum\limits_{i \in I_{xy}}r_{x,i}^2}\sqrt{\sum\limits_{i \in I_{xy}}r_{y,i}^2}}$$

The user based top-N recommendation algorithm identifies the k most similar users to an active user using similarity based vector model. After the k most similar users are found, their corresponding user-item matrices are aggregated to identify the set of items to be recommended. A popular method to find the similar users is the Locality sensitive hashing, which implements the nearest neighbor mechanism in linear time.

The advantages with this approach include: the explainability of the results, which is an important aspect of recommendation systems; it is easy to create and use; new data can be added easily and incrementally; it need not consider the content of the items being recommended; and the mechanism scales well with co-rated items.

There are several disadvantages with this approach. First, it depends on human ratings. Second, its performance decreases when data gets sparse, which is frequent with web related items. This prevents the scalability of this approach and has problems with large datasets. Third, it cannot handle new users or new items.

Model-Based
Models are developed using data mining, machine learning algorithms to find patterns based on training data. These are used to make predictions for real data. There are many model based CF algorithms. These include Bayesian Networks, clustering models, latent semantic models such as singular value decomposition, probabilistic latent semantic analysis, Multiple Multiplicative Factor, Latent Dirichlet allocation and markov decision process based models.

This approach has a more holistic goal to uncover latent factors that explain observed ratings. Most of the models are based on creating a classification or clustering technique to identify the user based on the test set. The number of the parameters can be reduced based on types of principal component analysis.

There are several advantages with this paradigm. It handles the sparsity better than memory based ones. This helps with scalability with large data sets. It improves the prediction performance. It gives an intuitive rationale for the recommendations.

The disadvantages with this approach are in the expensive model building. One needs to have a tradeoff between prediction performance and scalability. One can lose useful information due to reduction models. A number of models have difficulty explaining the predictions.

Hybrid
A number of applications combines the memory-based and the model-based CF algorithms. These overcome the limitations of native CF approaches. It improves the prediction performance. Importantly, it overcomes the CF problems such as sparsity and loss of information. However, they have increased complexity and are expensive to implement.

Data Sparsity
In practice, many commercial recommender systems are based on large datasets. As a result, the user-item matrix used for collaborative filtering could be extremely large and sparse, which brings about the challenges in the performances of the recommendation.

One typical problem caused by the data sparsity is the cold start problem. As collaborative filtering methods recommend items based on users’ past preferences, new users will need to rate sufficient number of items to enable the system to capture their preferences accurately and thus provides reliable recommendations.

Similarly, new items also have the same problem. When new items are added to system, they need to be rated by substantial number of users before they could be recommended to users who have similar tastes with the ones rated them. The new item problem does not limites the content-based recommendation, because the recommendation of an item is based on its description rather than its ratings, which will not be interfered by the insufficient of ratings. techniques.

Scalability
As the numbers of users and items grow, traditional CF algorithms will suffer serious scalability problems[ref?]. For example, with tens of millions of customers $$O(M)$$ and millions of items $$O(N)$$, a CF algorithm with the complexity of $$n$$ is already too large. As well, many systems need to react immediately to online requirements and make recommendations for all users regardless of their purchases and ratings history, which demands a higher scalability of a CF system.

Synonyms
Synonymy refers to the tendency of a number of the same or very similar items to have different names or entries. Most recommender systems are unable to discover this latent association and thus treat these products differently . For example, the seemingly different items “children movie” and “children film” are actually referring to the same item. Indeed, the degree of variability in descriptive term usage is greater than commonly suspected. The prevalence of synonyms decreases the recommendation performance of CF systems.

Grey Sheep
Gray sheep refers to the users whose opinions do not consistently agree or disagree with any group of people and thus do not benefit from collaborative filtering. Black sheep are the opposite group whose idiosyncratic tastes make recommendations nearly impossible. Although this is a failure of the recommender system, non-electronic recommenders also have great problems in these cases, so black sheep is an acceptable failure.

Shilling Attacks
In a recommendation system where everyone can give the ratings, people may give lots of positive ratings for their own items and negative ratings for their competitors. It is often necessary for the collaborative filtering systems to introduce precautions to discourage such kind of manipulations.

Innovations

 * New algorithms have been developed for CF as a result of the Netflix prize.
 * Cross-System Collaborative Filtering where user profiles across multiple recommender systems are combined in a privacy preserving manner.
 * Robust Collaborative Filtering, where recommendation is stable towards efforts of manipulation. This research area is still active and not completely solved.