Review Pulse

Getting genuine reviews in the contemporary world is quite a difficult task. User reviews play a crucial role in both web2 and web3 products and services, contributing to the success, trustworthiness and improvement of these offerings. Suppose you wish to buy a new mobile phone, wanna buy tickets for a new movie or wanna order a pizza from dominos, the first thing you look on as a user to the service is the customer reviews or ratings.

Any product which has better and more reviews provides more assurance of its trustworthiness and this is what the value of a review can be in the sale of a product and improvement of a service. On one side, the reviews add value to a product but there is no incentive for a user to add reviews. The problem revolves around the lack of sufficient incentives for users to provide honest and valuable reviews for products and services. Without proper motivation, users may not actively engage in the review process, leading to an incomplete or biased representation of the quality of products or services. Moreover, the absence of consequences for malicious reviews may undermine the trustworthiness of the entire review system. CHALLENGES:

1. Limited User Participation: Users may be reluctant to spend time and effort in writing reviews without a clear incentive or benefit.
2. Potential for Biased Reviews: Without proper checks and balances, there is a risk of receiving biased or fake reviews, which can mislead potential consumers.
3. Trust Issues: Users might be skeptical about the authenticity of reviews, affecting their trust in the review system. If there is some incentive for the user to provide honest reviews and punishments for the malicious providers, the review system can be more trustworthy and can add more value towards the improvement of the services.

Links

• https://github.com/Richa-iitr/Review-pulse-subgraph

• Coming with an algorithm for incentivizing the users given so many different parameters and constraints to consider such as the stake amount, reviewing time, closeness to majority.
• Integrating the project with Zero Knowledge Proofs to perform operations on non-disclosable ratings data.
• How to instantiate uniqueness of buyer i.e. preventing sybil attacks to increase or decrease the reviews, thankfully anon-adhar came to our rescue.
• dealing with dao contracts for governance, the max contract size got exceeded, we had to split the contracts and integrate them which was tough.
• A little unease for multichain interactions.