Data Con LA 2022 - Using Google trends data to build product recommendations
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The document details a presentation on leveraging Google Trends data for creating personalized product recommendations using advanced data signals and AI capabilities. It outlines a process of gathering trending search data, enriching it with AI/NLP services, and optimizing search and merchandising strategies for e-commerce applications. The approach aims to enhance product discovery and improve overall business performance by effectively responding to consumer trends.
![{
"id": "103661",
"title": "United",
"description": "United is based on the true story of Manchester United's
legendary Busby Babes, the youngest side ever to win the
Football League and the 1958 Munich Air Crash that claimed
eight of the their number. …."
"tags": [... "football (soccer)", "manchester united",
"historical event", "sports documentary"]
. . ..
}
Example catalog (extract)
https://movielens.org/
https://www.themoviedb.org/](https://image.slidesharecdn.com/datacon-la-2022-gcp-session-trends-2-recommendations-2022-aug-13b-rel-220908220720-8f71c3e3/85/Data-Con-LA-2022-Using-Google-trends-data-to-build-product-recommendations-34-320.jpg)
!["United is based on the true story of Manchester
United's legendary Busby Babes”
"Iron Man and Captain America battle to keep the
Red Skull and his triggerman, Taskmaster, from
unleashing an army”
"The Invincible Iron Man and the Incredible Hulk
must join forces to save the Earth from its
greatest threat yet”
Encoder
https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine
[-2.70270444e-02, -3.13250013e-02, 6.68615196e-03 …]
[8.240270444e-03, -5.11250213e-02, 7.68615196e-03 …]
[-7.60240444e-01, 4.28360013e-03, 7.68615196e-03 …]](https://image.slidesharecdn.com/datacon-la-2022-gcp-session-trends-2-recommendations-2022-aug-13b-rel-220908220720-8f71c3e3/85/Data-Con-LA-2022-Using-Google-trends-data-to-build-product-recommendations-42-320.jpg)
!["United is based on the true story of Manchester
United's legendary Busby Babes”
"Iron Man and Captain America battle to keep the
Red Skull and his triggerman, Taskmaster, from
unleashing an army”
"The Invincible Iron Man and the Incredible Hulk
must join forces to save the Earth from its
greatest threat yet”
Encoder
https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine
Input: Catalog Text Fragments (60K)
● Movie Title
● Movie Description
● Genres
● Tags
[-2.70270444e-02, -3.13250013e-02, 6.68615196e-03 …]
[8.240270444e-03, -5.11250213e-02, 7.68615196e-03 …]
[-7.60240444e-01, 4.28360013e-03, 7.68615196e-03 …]
Output: Embeddings (Vectors)
● Example: Leverage sentence-t5-base
● 60K Vectors @ 768 Dimensions
NLP Model (Transformer)
Prebuilt (HuggingFace, TFHub)
Custom (Two Tower, Swivel)
1 2](https://image.slidesharecdn.com/datacon-la-2022-gcp-session-trends-2-recommendations-2022-aug-13b-rel-220908220720-8f71c3e3/85/Data-Con-LA-2022-Using-Google-trends-data-to-build-product-recommendations-44-320.jpg)
!["United is based on the true story of Manchester
United's legendary Busby Babes”
"Iron Man and Captain America battle to keep the
Red Skull and his triggerman, Taskmaster, from
unleashing an army”
"The Invincible Iron Man and the Incredible Hulk
must join forces to save the Earth from its
greatest threat yet”
Encoder
https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine
Input: Catalog Text Fragments (60K)
● Movie Title
● Movie Description
● Genres
● Tags
[-2.70270444e-02, -3.13250013e-02, 6.68615196e-03 …]
[8.240270444e-03, -5.11250213e-02, 7.68615196e-03 …]
[-7.60240444e-01, 4.28360013e-03, 7.68615196e-03 …]
Output: Embeddings (Vectors)
● Example: Leverage sentence-t5-base
● 60K Vectors @ 768 Dimensions
NLP Model (Transformer)
Prebuilt (HuggingFace, TFHub)
Custom (Two Tower, Swivel)
1 2
3
Vertex AI
Matching
Engine
Index & Deploy!](https://image.slidesharecdn.com/datacon-la-2022-gcp-session-trends-2-recommendations-2022-aug-13b-rel-220908220720-8f71c3e3/85/Data-Con-LA-2022-Using-Google-trends-data-to-build-product-recommendations-45-320.jpg)
![from google.cloud import aiplatform
aiplatform.init()
my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(index_endpoint_name=<ID Here>)
from sentence_transformers import SentenceTransformer
NLP_MODEL = 'sentence-transformers/sentence-t5-base'
model = SentenceTransformer(NLP_MODEL)
search_term = 'Man United' # OPTIONAL: add text data from Enrichment Phase
query_sentences = [search_term]
embedding = model.encode(query_sentences)
DEPLOYED_INDEX_ID = "my_movielens_ndx_001"
response = my_index_endpoint.match(deployed_index_id=DEPLOYED_INDEX_ID,
queries=embedding,
num_neighbors=10)
print(response)
https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine
Python >= 3.6](https://image.slidesharecdn.com/datacon-la-2022-gcp-session-trends-2-recommendations-2022-aug-13b-rel-220908220720-8f71c3e3/85/Data-Con-LA-2022-Using-Google-trends-data-to-build-product-recommendations-47-320.jpg)
![from google.cloud import aiplatform
aiplatform.init()
my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(index_endpoint_name=<ID Here>)
from sentence_transformers import SentenceTransformer
NLP_MODEL = 'sentence-transformers/sentence-t5-base'
model = SentenceTransformer(NLP_MODEL)
search_term = 'Man United' # OPTIONAL: add text data from Enrichment Phase
query_sentences = [search_term]
embedding = model.encode(query_sentences)
DEPLOYED_INDEX_ID = "my_movielens_ndx_001"
response = my_index_endpoint.match(deployed_index_id=DEPLOYED_INDEX_ID,
queries=embedding,
num_neighbors=10)
print(response)
https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine
Python >= 3.6](https://image.slidesharecdn.com/datacon-la-2022-gcp-session-trends-2-recommendations-2022-aug-13b-rel-220908220720-8f71c3e3/85/Data-Con-LA-2022-Using-Google-trends-data-to-build-product-recommendations-48-320.jpg)
![from google.cloud import aiplatform
aiplatform.init()
my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(index_endpoint_name=<ID Here>)
from sentence_transformers import SentenceTransformer
NLP_MODEL = 'sentence-transformers/sentence-t5-base'
model = SentenceTransformer(NLP_MODEL)
search_term = 'Man United' # OPTIONAL: add text data from Enrichment Phase
query_sentences = [search_term]
embedding = model.encode(query_sentences)
DEPLOYED_INDEX_ID = "my_movielens_ndx_001"
response = my_index_endpoint.match(deployed_index_id=DEPLOYED_INDEX_ID,
queries=embedding,
num_neighbors=10)
print(response)
https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine
Python >= 3.6](https://image.slidesharecdn.com/datacon-la-2022-gcp-session-trends-2-recommendations-2022-aug-13b-rel-220908220720-8f71c3e3/85/Data-Con-LA-2022-Using-Google-trends-data-to-build-product-recommendations-49-320.jpg)
![from google.cloud import aiplatform
aiplatform.init()
my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(index_endpoint_name=<ID Here>)
from sentence_transformers import SentenceTransformer
NLP_MODEL = 'sentence-transformers/sentence-t5-base'
model = SentenceTransformer(NLP_MODEL)
search_term = 'Man United' # OPTIONAL: add text data from Enrichment Phase
query_sentences = [search_term]
embedding = model.encode(query_sentences)
DEPLOYED_INDEX_ID = "my_movielens_ndx_001"
response = my_index_endpoint.match(deployed_index_id=DEPLOYED_INDEX_ID,
queries=embedding,
num_neighbors=10)
print(response)
https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine
Python >= 3.6](https://image.slidesharecdn.com/datacon-la-2022-gcp-session-trends-2-recommendations-2022-aug-13b-rel-220908220720-8f71c3e3/85/Data-Con-LA-2022-Using-Google-trends-data-to-build-product-recommendations-50-320.jpg)
![[[MatchNeighbor(id='movielens-68194', distance=0.16755491495132446),
MatchNeighbor(id='movielens-185867', distance=0.16838324069976807),
MatchNeighbor(id='movielens-195337', distance=0.1685701608657837),
MatchNeighbor(id='movielens-145921', distance=0.17106527090072632),
MatchNeighbor(id='movielens-81676', distance=0.17208701372146606),
MatchNeighbor(id='movielens-103661', distance=0.17541170120239258),
MatchNeighbor(id='movielens-183153', distance=0.18686127662658691),
MatchNeighbor(id='movielens-147436', distance=0.1904624104499817),
MatchNeighbor(id='movielens-176405', distance=0.1911325454711914),
MatchNeighbor(id='movielens-6618', distance=0.1932288408279419)]]
Cosine Distance = 1 — Cosine Similarity
Man United
Iron Men](https://image.slidesharecdn.com/datacon-la-2022-gcp-session-trends-2-recommendations-2022-aug-13b-rel-220908220720-8f71c3e3/85/Data-Con-LA-2022-Using-Google-trends-data-to-build-product-recommendations-51-320.jpg)
Data Con LA 2022 - Using Google trends data to build product recommendations
- 2. Presented by: Mike Limcaco Contributions: Lexi Flynn & Bryan Rutkowski 2022 August 13 DataCon LA 2022 Trends To Recommendations Using Google trends data to build product recommendations
- 5. How can we tap into what’s trending & continuously promote new, interesting and relevant content? 1. Aggregate trending search interest 2. Turn these into smart searches against our product catalog(s) 3. Filter, sort and rank to further refine results
- 6. Leverage Google-Unique Data & APIs To Drive Personalized Discovery Data Signals Aggregated search trend data across geographic focus areas Retail Search Power your ecommerce site or application with Google-quality search capabilities Vertex AI Matching Engine Use innovative semantic matching features to improve search results
- 7. Data Signals Aggregated search trend data across geographic focus areas Retail Search Power your ecommerce site or application with Google-quality search capabilities Vertex AI Matching Engine Use innovative semantic matching features to improve search results Leverage Google-Unique Data & APIs To Drive Personalized Discovery
- 8. What is BigQuery Data Signals? Google Trends Data Signals BigQuery Public Datasets Program Insights for Everyone Marketing How do I optimize my paid media spend when it comes to search term bidding? Merchandising & Demand Forecasting How can I understand demand trends to make better assortment planning decisions? Executives How is the business performing across all of our markets? Against competitors? Supply Chain How do I manage supply chain logistics and distribute inventory better?
- 10. Data Signals Aggregated search trend data across geographic focus areas Retail Search Power your ecommerce site or application with Google-quality search capabilities Vertex AI Matching Engine Use innovative semantic matching features to improve search results Leverage Google-Unique Data & APIs To Drive Personalized Discovery
- 11. Assistant Text search Google Cloud Retail Search High-Quality Product Search Results as a Service Typeahead Product Catalog User Events, Logs Custom LTR Models Query Understanding Web + Structured Index Pricing Updates Business / Config Rules Your Team Retail Search
- 12. Data Signals Aggregated search trend data across geographic focus areas Retail Search Power your ecommerce site or application with Google-quality search capabilities Vertex AI Matching Engine Use innovative semantic matching features to improve search results Leverage Google-Unique Data & APIs To Drive Personalized Discovery
- 13. Google Cloud Vertex AI: Matching Engine Scalable & cost-effective Vector similarity matching service Low Latency Find nearest neighbors in milliseconds Scalable Scales to billions of vectors Cost Effective Requires fewer VMs to serve the same workload ● 1/4th the CPU consumption of faiss ● 1/3rd the memory consumption of nmslib Google’s technology (labelled ScaNN) compared with popular ANN services Scalable Nearest Neighbors https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html
- 14. 1. Training Data Labeled Pairs
- 16. Putting it all together Signal Gather trending search data (Trends) via Google Cloud BigQuery Enrich Leverage AI / NLP services to enrich the term (entity processing) Search Search your product catalog with enriched terms Refine Refine results by scoring against additional semantic relevance
- 17. Putting it all together Signal Gather trending search data (Trends) via Google Cloud BigQuery Enrich Leverage AI / NLP services to enrich the term (entity processing) Search Search your product catalog with enriched terms Refine Refine results by scoring against additional semantic relevance BigQuery Vertex AI / Retail Search Vertex AI Natural Language
- 18. Google Data Signals (BQ) Search Term 1 | Location | DateTime Search Term 2 | Location | DateTime Search Term ... Extract entities Salience Wikipedia / KG BigQuery Filter & Enrichment Refine End User or App Experience Product Catalog 1B 3 Catalog-ID 1 | Title | Description Catalog-ID 2 | Title | Description . .. Terms Enriched Terms Enriched Search (Example Only): select Catalog-ID from Catalog where Metadata like ‘<Search Term>’ and Genre = “<Genre>” Semantically Relevant Video Watch List Search Retail AI Search 2 Natural Language API Vertex AI Vector Similarity 1A Putting it all together
- 19. Example
- 20. Example: Man United Wikipedia: Manchester United Football Club, commonly referred to as Man United, or simply United, is a professional football club based in the Old Trafford area of Manchester, England. The club competes in the Premier League, the top division in the English football league system. https://trends.google.com/trends/explore?geo=US-CA-803&q=man%20united https://en.wikipedia.org/wiki/Manchester_United_F.C.
- 21. Trending in Los Angeles “Manchester United” Retail Ticketing & Live Events Video / OTT Streaming You might like …
- 22. Video / OTT: Trends Influencing Top Shelf 1. Respond to community search trends / interest in “Man United” ○ Sports team touring and visiting Los Angeles 2. Generate search candidates against sample Catalog (movielens.org) ○ Return Movies & Products involving Manchester United FC 3. Create a tailored “Recommended / Hot” Shelf on App Landing page ○ Filter to include Products that are thematically related (sports, football / soccer, documentary, action etc)
- 25. SELECT * FROM `bigquery-public-data.google_trends.top_terms` WHERE refresh_date = DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY) and dma_name = 'Los Angeles CA' ORDER BY week DESC, rank ASC https://cloud.google.com/bigquery/docs/sandbox https://console.cloud.google.com/marketplace/product/bigquery-public-datasets/google-search-trends
- 31. Optional: Enrich the Catalog Search $ gcloud ml language analyze-entities --content="Man United vs. Liverpool" { "entities": [ { … "metadata": { "mid": "/m/050fh", "wikipedia_url": "https://en.wikipedia.org/wiki/Manchester_United_F.C." }, "name": "Man United", "salience": 0.8100557, "type": "ORGANIZATION" }... "metadata": { "mid": "/m/04ltf", "wikipedia_url": "https://en.wikipedia.org/wiki/Liverpool_F.C." }.. https://cloud.google.com/sdk/gcloud/reference/ml
- 34. { "id": "103661", "title": "United", "description": "United is based on the true story of Manchester United's legendary Busby Babes, the youngest side ever to win the Football League and the 1958 Munich Air Crash that claimed eight of the their number. …." "tags": [... "football (soccer)", "manchester united", "historical event", "sports documentary"] . . .. } Example catalog (extract) https://movielens.org/ https://www.themoviedb.org/
- 37. “Man United”
- 39. ”Man United” related video content & movies where core metadata matches (token match) but there is content here that is potentially thematically “distant” https://console.cloud.google.com/ai/retail
- 42. "United is based on the true story of Manchester United's legendary Busby Babes” "Iron Man and Captain America battle to keep the Red Skull and his triggerman, Taskmaster, from unleashing an army” "The Invincible Iron Man and the Incredible Hulk must join forces to save the Earth from its greatest threat yet” Encoder https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine [-2.70270444e-02, -3.13250013e-02, 6.68615196e-03 …] [8.240270444e-03, -5.11250213e-02, 7.68615196e-03 …] [-7.60240444e-01, 4.28360013e-03, 7.68615196e-03 …]
- 43. "United is based on the true story of Manchester United's legendary Busby Babes” "Iron Man and Captain America battle to keep the Red Skull and his triggerman, Taskmaster, from unleashing an army” "The Invincible Iron Man and the Incredible Hulk must join forces to save the Earth from its greatest threat yet” Encoder https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine Input: Catalog Text Fragments (60K) ● Movie Title ● Movie Description ● Genres ● Tags 1
- 44. "United is based on the true story of Manchester United's legendary Busby Babes” "Iron Man and Captain America battle to keep the Red Skull and his triggerman, Taskmaster, from unleashing an army” "The Invincible Iron Man and the Incredible Hulk must join forces to save the Earth from its greatest threat yet” Encoder https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine Input: Catalog Text Fragments (60K) ● Movie Title ● Movie Description ● Genres ● Tags [-2.70270444e-02, -3.13250013e-02, 6.68615196e-03 …] [8.240270444e-03, -5.11250213e-02, 7.68615196e-03 …] [-7.60240444e-01, 4.28360013e-03, 7.68615196e-03 …] Output: Embeddings (Vectors) ● Example: Leverage sentence-t5-base ● 60K Vectors @ 768 Dimensions NLP Model (Transformer) Prebuilt (HuggingFace, TFHub) Custom (Two Tower, Swivel) 1 2
- 45. "United is based on the true story of Manchester United's legendary Busby Babes” "Iron Man and Captain America battle to keep the Red Skull and his triggerman, Taskmaster, from unleashing an army” "The Invincible Iron Man and the Incredible Hulk must join forces to save the Earth from its greatest threat yet” Encoder https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine Input: Catalog Text Fragments (60K) ● Movie Title ● Movie Description ● Genres ● Tags [-2.70270444e-02, -3.13250013e-02, 6.68615196e-03 …] [8.240270444e-03, -5.11250213e-02, 7.68615196e-03 …] [-7.60240444e-01, 4.28360013e-03, 7.68615196e-03 …] Output: Embeddings (Vectors) ● Example: Leverage sentence-t5-base ● 60K Vectors @ 768 Dimensions NLP Model (Transformer) Prebuilt (HuggingFace, TFHub) Custom (Two Tower, Swivel) 1 2 3 Vertex AI Matching Engine Index & Deploy!
- 47. from google.cloud import aiplatform aiplatform.init() my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(index_endpoint_name=<ID Here>) from sentence_transformers import SentenceTransformer NLP_MODEL = 'sentence-transformers/sentence-t5-base' model = SentenceTransformer(NLP_MODEL) search_term = 'Man United' # OPTIONAL: add text data from Enrichment Phase query_sentences = [search_term] embedding = model.encode(query_sentences) DEPLOYED_INDEX_ID = "my_movielens_ndx_001" response = my_index_endpoint.match(deployed_index_id=DEPLOYED_INDEX_ID, queries=embedding, num_neighbors=10) print(response) https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine Python >= 3.6
- 48. from google.cloud import aiplatform aiplatform.init() my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(index_endpoint_name=<ID Here>) from sentence_transformers import SentenceTransformer NLP_MODEL = 'sentence-transformers/sentence-t5-base' model = SentenceTransformer(NLP_MODEL) search_term = 'Man United' # OPTIONAL: add text data from Enrichment Phase query_sentences = [search_term] embedding = model.encode(query_sentences) DEPLOYED_INDEX_ID = "my_movielens_ndx_001" response = my_index_endpoint.match(deployed_index_id=DEPLOYED_INDEX_ID, queries=embedding, num_neighbors=10) print(response) https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine Python >= 3.6
- 49. from google.cloud import aiplatform aiplatform.init() my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(index_endpoint_name=<ID Here>) from sentence_transformers import SentenceTransformer NLP_MODEL = 'sentence-transformers/sentence-t5-base' model = SentenceTransformer(NLP_MODEL) search_term = 'Man United' # OPTIONAL: add text data from Enrichment Phase query_sentences = [search_term] embedding = model.encode(query_sentences) DEPLOYED_INDEX_ID = "my_movielens_ndx_001" response = my_index_endpoint.match(deployed_index_id=DEPLOYED_INDEX_ID, queries=embedding, num_neighbors=10) print(response) https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine Python >= 3.6
- 50. from google.cloud import aiplatform aiplatform.init() my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(index_endpoint_name=<ID Here>) from sentence_transformers import SentenceTransformer NLP_MODEL = 'sentence-transformers/sentence-t5-base' model = SentenceTransformer(NLP_MODEL) search_term = 'Man United' # OPTIONAL: add text data from Enrichment Phase query_sentences = [search_term] embedding = model.encode(query_sentences) DEPLOYED_INDEX_ID = "my_movielens_ndx_001" response = my_index_endpoint.match(deployed_index_id=DEPLOYED_INDEX_ID, queries=embedding, num_neighbors=10) print(response) https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official/matching_engine Python >= 3.6
- 51. [[MatchNeighbor(id='movielens-68194', distance=0.16755491495132446), MatchNeighbor(id='movielens-185867', distance=0.16838324069976807), MatchNeighbor(id='movielens-195337', distance=0.1685701608657837), MatchNeighbor(id='movielens-145921', distance=0.17106527090072632), MatchNeighbor(id='movielens-81676', distance=0.17208701372146606), MatchNeighbor(id='movielens-103661', distance=0.17541170120239258), MatchNeighbor(id='movielens-183153', distance=0.18686127662658691), MatchNeighbor(id='movielens-147436', distance=0.1904624104499817), MatchNeighbor(id='movielens-176405', distance=0.1911325454711914), MatchNeighbor(id='movielens-6618', distance=0.1932288408279419)]] Cosine Distance = 1 — Cosine Similarity Man United Iron Men
- 53. Refine: Semantic Match & Add Add Candidate Title ● Semantic Match = YES Delete Candidate Title ● Semantic Match = NO
- 55. Discussion
- 56. Commentary & Summary 1. Overall Strategy a. Trending Data Signals (Search) to “seed” Content Recommendations b. Search Engine (Google Cloud Retail Search AI) to gather candidates c. Vector Similarity (Google Cloud Vertex AI) to rank sort candidates 2. Part of larger solution! a. Ex: Use collaborative filtering (or other) to gauge user interest / engagement b. Good for cold-start or content-heavy approach 3. More details available a. https://console.cloud.google.com/marketplace/product/bigquery-public-datasets/google-search-trends b. https://cloud.google.com/solutions/retail-product-discovery c. https://cloud.google.com/vertex-ai/docs/matching-engine/overview