With the evolution of technology, online retail shopping has come into action, playing a major role in the modern world. A personalized recommendation system aims at identifying products that are of most relevance to a user, based on their past interactions.
This enhances a user’s intention to browse more products and makes them more likely to buy these products, effectively increasing business revenue and user experience. Hence, it is of vital importance that the evaluation of recommendations in such a context provides an end user output based on criteria which is selected in a way that maximizes business revenue and user experience. This chosen ‘most optimal criteria’ may vary due to different user preferences, seasons, and many other factors. Therefore, selecting the most optimal criteria has to be done very thoroughly, for which an effective and efficient evaluation technique is essential.
Where Do You Stand?
In this fast-moving modern world, People tend to buy online due to their busy schedules and easement and any outdated organization that doesn’t support this will be left behind. In a post Covid-19 world, online retailing and e-commerce without a doubt will increase immensely, forcing almost every organization to use online retailing for survival. Recommendation systems play a very important role in this, helping out with revenue and user experience. All the leading retailers worldwide use modern recommendation systems. It is definite that online retailers that use primitive recommendation systems will not be competitive enough to survive among the others who already use standard recommendation systems.
Multi Armed Bandit
Evaluation of recommendations can be categorized into two: offline evaluation and online evaluation. An example for offline evaluation is the Multivariate Testing Method which allows exploration of the most optimal criteria within a specific period of time, but afterward serves recommendations using the winning criteria. Hence it only provides a single cycle of exploration to exploitation, and does not allow automated further exploration cycles. This leads to a requirement of manual intervention once the criteria pass its optimal performance. These limitations bring out the necessity of online evaluation that supports automated multiple exploration cycles, which leads us to Multi Armed Bandit. The Multi Armed Bandit problem is a concept where a fixed limited set of resources are to be allocated among competing choices in a manner that maximizes their expected gain.
Multi Armed Bandit In A Retail Context
The endless expansion of e-commerce has led retailers to advertise their products by displaying. This is done via recommendation after considering various factors. Recommendation systems are growing progressively in the field of online retail due to their capability in offering personalized experiences to unique users. They make it easier for users to access the content they are interested in, which results in a competitive advantage for the retailer. Hence it is necessary to have smart recommendation systems. Recommendation systems using Multi Armed Bandit are capable of continuous learning, that is continuously exploring winning criteria and exploiting them without manual intervention.
What We At Zone24x7 Do
We excel in offering smart recommendation systems. We are well experienced in coming up with recommendation systems that give out different results to the user each day by processing massive loads of data in the intelligent back-end. We have studied every possible way to do that and selected 3 effective algorithms to the MAB problem, which are in summary:
- Epsilon Greedy Algorithms
- Upper Confidence Bound Algorithms (UCB)
- Thompson Sampling
We chose Thompson Sampling for the retail recommendation system and it has been one of the highest performing solutions due to less cumulative regret. It is also the highest cost-effective solution when it comes to implementation.
Multi Armed Bandit can be recognized as the core ideology of the online evaluation system and only a brief explanation about it is given here.
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