Wish Discover


User Goal

Discover products that I want and need.

Business Goal

Increase add-to-cart volume, conversion rate, and gross merchandise value.


Pitched the concept to the leadership team; worked on the execution strategy, user research, data analysis, UI/UX design; and refined the recommendation machine learning model with engineers.


Increased 3+% gross merchandise value.

User pain points via App Store review

Wish has a large assortment of products; however, too many product offerings and choices can exhaust customers, leading to frustration (choice overload). “Filters” and “categories” are the top two feature requests from users in improving the shopping experience.

App Store Review

Shipped filters and categories experiment (V1)

Quick experiments are performed on the production system to quantify their effectiveness.

Wish Filters Wish categories

Results: V1 failed

Gross merchandise volume drops, leading to a deeper dive into the reasons and possible solutions.

Why did filters fail?
When users land on the homepage, they haven’t decided what they want to buy, so filters don’t work well here.

Why did categories fail?
Categories are not the most effective way to browse through millions of product offerings.

V1 conclusion
With the gathered data and insights, we decided to focus on the search function.

Searching is a popular channel in finding products, but this method needs improvement

pie chart

Analyze users’ searching behavior when conducting a query

To find insight into the searching behavior, I analyzed the raw queries of each user. I studied what they search, how they search, and the correlations between queries. This study also generates a report on how products should be organized and grouped to maximize the effectiveness of product placement in our UI.

Wish Shopper Search Pattern

Tree-structure v.s. net-structure browsing

The shopper searching pattern resembles a net-structure instead of a tree-structure. This slight misalignment drops the gross merchandise value when a category (a tree-structure) is introduced.

Search pattern Findings

A net-based browsing UI: Tags

How might we introduce a net-based browsing concept with a simple user interface? Fortunately, I did not need to search very long because tag-based browsing was gaining traction when my project started.

 Competitor UI

Partnered with machine learning engineers & served as a strong advocate for user behaviors and business priorities

The critical success factors are the relevancy of these tags and the corresponding products that are displayed; however, the patterns are too convoluted to code in the programming logic. I got the buy-in from the machine learning team and initiated a joint project to tackle the problem.

Machine Learning

I served as a strong advocate for user behaviors and business priorities in the machine learning team. For each model training iteration, I would identify the weaknesses of the trained model. Then, we would engage in joint brainstorming sessions to refine the model training objectives and to add new candidate selection and ranking rules.

Here are some examples for the illustration:

Launched Search Tag (V2): relevancy of the displayed search tags and product results needs further improvement

Launched V1 Quantitative Metrics
Qualitative Feedback through User Interviews Findings: It reinforced our belief that the relevancy of the displayed search tags and product results needs further improvement.

Tag UI explorations

I experimented with different UI elements and flows to increase the tag's click rate.


Create a benchmark to measure quality of tags, enumerating the Google search tag in the production

To gauge the quality of our search tag, I established a benchmark in comparing our search tag with the Google image search tag. I conducted user research to collect their opinions on which was better and why. Also, I created plans to collect analytical data by enumerating the Google search tag in the production. These experiments allowed me to set up concrete goals for model improvement and quantified its ROI. Another key finding was that we failed to consolidate the statistic for similar queries. It hurt the ranking score and had huge impacts on which tags are selected for displacement. For V3, we prioritized the improvement in the quality of the search tags.

Google tags VS Wish Tags

Launched V3 with better quality search tags


Future improvement

Key focus in the future will be:


Don’t just pass off responsibility to the machine learning engineers. Instead, work collaboratively to improve the models iteratively. Commit your skill and domain knowledge to refining the training objectives. The success of the project depends on it.

Other takeaways

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