Different Recommendation approaches are briefed with their advantages limitations and challenges.

1. Approaches for
Recommendation System
By:- Vikash Kumar
NIT Durgapur

2. Types of Recommendation System
1. Collaborative Filtering(CF)
2. Content-Based Filtering (CBF)
3. Hybrid Recommendation approach

3. Collaborative Filtering.
Collaborative Filtering algorithm is basically separated into two parts.
● Model based algorithm:- Cluster Model, Bayesian Neural Network Model
● Memory based algorithm:- Item based filtering, User based filtering

4. Memory Based Collaborative Filtering
1. Recommends items according to the rating behavior of users.
2. Quality of the CF model improves with time.
Challenges:-
1. This approach does not work efficiently in sparse datasets which denotes less
number of ratings on the items.
2. It suffers from Cold start problem that means there is no rating for newly
added items and newly added users do not rated any item then how
recommendations is possible for these kinds of users and items.
3. Scalability.

5. Model Based CF
1. Model based approach deals with sparsity and scalability issues. But it is an
expensive implementation technique.
2. The loss of information takes place when dimensionality reduction techniques
are used.

6. Content Based Filtering

7. Advantages Content based filtering..
1. User independence: Collaborative filtering needs other users rating to find the
similarity between the users and then give the suggestion. Instead, content-
based method only have to analyze the items and user profile for
recommendation.
2. Transparency: collaborative method gives you the recommendation
because some unknown users have the same taste like you, but content-
based method can tell you they recommend you the items based on what
features.
3. No cold start problem.

8. Limitation of Content Based in filtering.
1. If the content does not contain enough information to discriminate the items
precisely.
2. When there's not enough information to build a solid profile for a user, the
recommendation could not be provided correctly.

9. Why Community detection in recommendation
system?
Community Detection approach in recommendation system deals with the
following issues:-
1. Scalability
2. Coverage
3. Cold Start Problem
Personalized Recommendation is also possible in Community-based
recommendation system.
This technique follows the epigram “Tell me who your friends are, and I will
tell you who you are” .

10. Challenge in community detection approach.
Fig shows issue related to static community detection

11. Moving towards Dynamic Community Detection
Approach.
Used to identify community structures and their development over time.
Along with the advantage of the static community, this approach can deal with
real-world networks as it takes into account the evolutionary aspect of the users’
interests over time.

12. Dynamically changing communities..

13. Challenges in dynamic communities approach.
1. Most of the existing algorithms do not consider above operations on
community, as they are designed for static community.
2. Defining dynamic communities is more complex as they are not just nodes
and edges.
3. Multiple snapshots of community is needed.

14. Community Overlapping is the issue for dynamic
community..
Main Challenges involved in overlapping community.
1. Identifying the nodes that are common in two or more communities .
2. Identifying the degree of association of node to a particular community.