Recommendation Architecture: Adaptive Hybrid: Implicit Social Profiling with Synergizing RNN to Mitigate cold start

The Recommender Systems (RS) have taken the shape of modern digital user
experience stalwart though they keep experiencing a challenge which remains very critical and
stubborn the Cold Start challenge. Once a new user enters a platform, the historical interaction
information is not available and this makes traditional Collaborative Filtering algorithms
useless. The current solutions will tend to use the static demographic data which cannot reflect
the dynamism of the user preferences. This paper posits that the problem is not needing more
explicit cues in users, but ought to seek to exploit better the implicit cues users do produce.
Our suggestion is Two Stages Content-Boosted Collaborative Filtering (TS-CBCF). At the first
stage we use implicit Social Profiling, where we extract behavioral phenotypes (e.g. activity
timing, social connectivity) to construct a full initial user matrix. The user engages with the
system and we enter a Deep Learning phase with the help of the Long Short-Term Memory
(LSTM) networks to observe the preference evolution. We provide a detailed comparative
study with the state-of-the-art methodologies, and we prove that our hybrid framework
provides a higher degree of resilience in high sparsity settings coupled with more advanced
privacy and scalability issues of standard ones.