• Kshitij Nipendra Karekar
• Kavita Jain
• Janeya Limbachia
• Nadar Jaysalviya Salwin

A user based travel guide recommendation system is proposed in this paper. We have proposed a novel method to generate a hybrid recommendation list for the user. Technologies mainly used for recommendation systems are collaborative filtering. Collaborative filtering are of two types: User - User and Item - Item. Item based filtering technique is preferred in recommender systems; however it gives a monotonous list. We have proposed a solution where in we will combine both user - user and item - item filtering and get a hybrid recommendation. The unique part of this recommender system will be that it will use click stream for foreign and PCA for internal user behavior. The proposed system will be efficient and predict better than the traditional recommender systems.

Cosine Similarity, K-Nearest Neighbor(KNN), Collaborative Filtering(CF), Item-Based Collaborative Filtering(IBCF), User-Based Collaborative Filtering(UBCF), PCA, Click-Stream, Machine Learning(ML), Android Application.

"Trail Traveller", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.a420-a425, May-2021, Available :http://www.jetir.org/papers/JETIR2105053.pdf

"Trail Traveller", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppa420-a425, May-2021, Available at : http://www.jetir.org/papers/JETIR2105053.pdf