This section describes normalization techniques, similarity measures and user-based and item-based methods to form user clusters and item clusters which will be

This section describes about the dissimilarity measure used, for mation o f sessions, formation of user-based clusters and item-based clusters, recommendation of

The techniques of item-based collaborative filtering recommendation system and Matrix factorization collaborative filtering recommendation system was compared and

(i.e., they either rate different items similarly or they tend to buy similar set of items). Once a neighborhood of users is formed, these systems use different algorithms

This paper presents asocial recommendation approach that exploits individual relationship networks (IRN’s) for users and items to address the huge size, sparsity, imbalance and

This paper investigated recommendation techniques that include content-based recommendation technique, collaborative (social) filtering technique, hybrid recommendation

Strong community partnerships between the local Aboriginal or Islander community and school staff is vital to embed Aboriginal and Torres Strait Islander perspectives across

A new CF recommendation algorithm based on dimensionality reduction and clustering techniques has been proposed in [7] using the k-means algorithm and Singular Value

Figure 2.1 General structure of the literature review 6 Figure 2.2 Simple flow of Content-based filtering 7 Figure 2.3 Simple flow of collaborative filtering 9 Figure 2.4

2) The rest 80% of the data goes through sparsity removal and GA-SOM clustering. For GA, the soft penalty limits the minimum and maximum number of clusters to be formed at 4 and 10

Collaborative filtering is mainly based on two Types of techniques; they are Memory-Based or User Based Collaborative Filtering and Model- Based or Item Based Collaborative

Collaborative Filtering, which is based on items uses two techniques- Pearson correlation technique and Adjusted cosine technique for calculating the similarity between items and

This type of recommendation system works with the data that is being provided by the user either by rating given to a product or by determining the nature of the sentence by

Experiments include the results of evaluating item-based collaborative filtering (IICF) (Sarwar, Karypis, Konstan, & Riedl, 2001), user-based collaborative

This type of recommendation system works with the data that is being provided by the user either by rating given to a product or by determining the nature of the sentence by

Jianfeng Hu [6] proposed product recommendation based on the collaborative filtering, in specific user based collaborative filtering, which starts by finding a set

In this paper, based on the traditional collaborative filtering algorithm we classify the similarity into indirect similarity and indirect similarity, then the

Change the Record is an unprecedented coalition of leading Aboriginal and Torres Strait Islander, human rights, legal and community organisations calling for urgent and