Detecting user intent shifts using vector distances involves comparing the semantic similarity of user inputs over time. The core idea is to represent each user message as a vector (embedding) in a high-dimensional space and measure the distance between consecutive or related vectors. If the distance between two vectors exceeds a predefined threshold, it indicates a potential shift in intent. For example, in a chatbot conversation, a user might start by asking about billing and then abruptly switch to technical support. By encoding these messages into vectors and calculating their similarity (e.g., using cosine distance), you can identify when the topic has changed significantly.
To implement this, first convert user messages into embeddings using a pre-trained model like Sentence-BERT, Universal Sentence Encoder, or OpenAI’s text-embedding models. These models map text to vectors that capture semantic meaning. Next, calculate the distance between the current message’s vector and previous ones. Cosine similarity is a common metric—it measures the angle between vectors, with lower values (closer to 0) indicating higher similarity. For example, if a user’s first message (“How do I reset my password?”) has a cosine distance of 0.8 from their next message (“What’s the status of my order?”), the large distance suggests a shift from account help to order tracking. You can track distances over a sliding window of recent messages to account for gradual context changes.
Practical considerations include choosing the right embedding model, setting appropriate thresholds, and handling noise. For instance, a customer support bot might use a lower threshold (e.g., 0.3 cosine distance) to flag subtle shifts, while a news summarizer could tolerate higher distances. Testing with real data is critical: collect sample conversations, compute distances, and adjust thresholds to balance false positives (flagging non-shifts) and false negatives (missing actual shifts). Additionally, combining vector distances with other signals—like keyword matching or sequence-based models (e.g., LSTMs)—can improve accuracy. For example, a sudden distance spike paired with a keyword like “cancel” might prioritize handling subscription-related intent shifts. Tools like scikit-learn or libraries such as FAISS can streamline vector comparisons at scale.