Park, fuzzy Bayesian networks and utility theory. The

Park, Ji-Oh and
Sung-Bae el al.  10, uses fuzzy
Bayesian Network to propose a context-aware music recommendation system. In
Context-aware music recommendation system, Park, Ji-Oh and Sung-Bae el al.
10, they are using fuzzy Bayesian networks and utility theory. The
context-aware music recommendation system (CA-MARS) exploits fuzzy system to
incorporate diverse source of information. While providing recommendation to
user, context also plays vital role for any decision system (example:
Temperature, Humidity, Noise, Season etc.). The problem with Bayesian Network
is it cannot deal with the diverse information. There is a possibility of loss
of information and it may not reflect the context appropriately. To overcome
this limitation, Park, Ji-Oh and Sung-Bae el al. 10 are proposing fuzzy
system.

 

Gaikwad et al.
11, are proposing food recommendation system based on use profile (Age,
Gender and Profession). They are trying to develop intelligent recommendation
system which can give more personalized result. To train the input data, Gaikwad
et al. 11, are using predictive data mining methods. Feed Forward Algorithm
(FF) and Back-Propagation Neural Network (BPNN) is used as an algorithm. They
are arguing that there exists some food recommendation system and they provide
recommendation only based on the likes and rating of foods/restaurants. The
limitation of Gaikwad et al. 11, is that the choice of user may change
depending on the context. This system 11 doesn’t recommend food on the basis
of context parameters. Only using user’s personal data like: age, gender,
profession is not enough to provide recommendation of best food.  It is often observed that the food choice
varies with particular factors like age group, gender, health, ethnic group,
etc. In addition to this, context variables like season, time, weather and
temperature plays vital role in the person’s behavior of the food choices.

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Swant et al. proposed food recommendation system 12 implementing
hybrid cascade of K-nearest neighbor clustering and weighted bipartite graph
projection. They are using Yelp’s open dataset 13 to retrieve actual
business, user, and users’ review data from the greater Phoenix, AZ metropolitan
area. By using various data fields, they identify similar business and users to
aggregate the likely sparse ratings per average user. In their approach, K-Nearest
neighbor clustering is applied for business and users individually. Application
maps each business to its specific cluster C and checks which specific user
from user cluster had rated food and average together their rating. Which use
to calculate the predicted rating for user for the business. Although this
paper generates the comparatively good result by only taking data of user
rating and business, implementation of context-based application would be more
accurate and realistic because parameters like season, time, weather,
temperature etc. plays significant role on food selection. Hence, consideration
of user preference and the context will improve the usability of the system.